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Rocket Lawyer

<div><div><div>About Rocket Lawyer</div>
<div></div>
<div>We believe everyone deserves access to affordable and simple legal services. Founded in 2008, Rocket Lawyer is the largest and most widely used online legal service platform in the world. With offices in North America, South America, and Europe, Rocket Lawyer has helped over 30 million people create over 50 million legal documents, and get their legal questions answered.</div>
<div></div>
<div>We are in a unique position to enhance and expand the Rocket Lawyer platform to a scale never seen before in the company's history, to capture audiences worldwide. We are expanding our team to take on this challenge!</div></div>
About your role

Rocket Lawyer is looking for a motivated and passionate AI/ML Software Engineering Intern to join our team working on Rocket Copilot, our AI assistant launched in December 2023. In this internship, you will collaborate with our cross-functional teams to assist in building a cutting edge AI platform. You'll have the opportunity to learn from experienced engineers while working on real-world AI/ML projects, helping design, implement, test, and maintain new AI features.

We value a collaborative and supportive work environment, where we celebrate learning and growth. This is a great opportunity for someone eager to expand their skills and contribute to innovative AI solutions.

Responsibilities
<ul>
<li>Assist in prompting, evaluating, fine-tuning, and maintaining large language models (LLMs) for AI features</li>
<li>Support the creation, monitoring, and troubleshooting of backend API services that interact with ML models</li>
<li>Collaborate with cross-functional teams to integrate AI into existing and new Rocket Lawyer products</li>
<li>Research and stay up-to-date with industry trends around generative AI and machine learning</li>
<li>Prototype product features to explore new AI capabilities and write clean, understandable code</li>
</ul>
What you'll need
<ul>
<li>Currently pursuing a BS, MS, or PhD in Computer Science, Mathematics, Statistics, or a related field.</li>
<li>Basic understanding of machine learning concepts and algorithms.</li>
<li>Familiarity with generative AI technologies and models (Deep Learning, RAG, Neural Networks, Agents).</li>
<li>Interest or experience working with large language models (LLMs) and generative AI.</li>
<li>Strong programming skills in Python</li>
</ul><h3 class="jobSectionHeader">A bonus if you have</h3>
<ul><li>Experience with backend Python frameworks like FastAPI, Flask, Django, etc.</li>
<li>Familiarity with vector databases.</li>
<li>Additional programming skills in languages such as Java, Kotlin, or NodeJS.</li>
<li>SQL knowledge.</li>
<li>Interest in building user-facing products.</li>
<li>Understanding of microservices architectures and distributed systems.</li>
</ul>
Interview Process
<ul>
<li>Recruiter Phone Screen</li>
<li>Hiring Manager Interview</li>
<li>Team Interview</li>
</ul><div><div><div><div><div><div>Rocket Lawyer is proudly committed to recruiting and retaining a diverse and inclusive workforce. As an Equal Opportunity Employer, we never discriminate based on race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, military or veteran status, status as an individual with a disability, or other applicable legally protected characteristics. We particularly welcome applications from veterans and military spouses.</div>
<div></div>
<div>All your information will be kept confidential according to EEO guidelines.
</div><div>You may request reasonable accommodations by sending an email to hr@rocketlawyer.com.</div>
<div></div>
<div>Location: Remote, but you must be located in CA, AZ, CO, NC, or UT during the internship. 
Duration: June 8, 2026 - August 14, 2026, working 40 hours per week</div>
<div></div>
<div><div>Compensation per hour by location:</div>
<ul><li>San Francisco Bay Area, CA: $50.00</li>
<li>California (outside of San Francisco Bay Area): $46.25</li>
<li>Colorado: $42.50</li>
<li>Utah, Arizona, and North Carolina: $40.00</li></ul> 
</div>
</div></div></div></div></div><div></div><div>By applying for this position, your data will be processed as per Rocket Lawyer Privacy Policy.</div></div>

AI/ML Intern

DigitalOcean

<div><div>Dive in and do the best work of your career at DigitalOcean. Journey alongside a strong community of top talent who are relentless in their drive to build the simplest scalable cloud. If you have a growth mindset, naturally like to think big and bold, and are energized by the fast-paced environment of a true industry disruptor, you'll find your place here. We value winning together&mdash;while learning, having fun, and making a profound difference for the dreamers and builders in the world.</div><h2 class="jobSectionHeader">
We are looking for a Manager, Customer Success Engineering (AI &amp; Cloud Support) who is passionate about delivering exceptional support experiences and building high-performing teams.</h2>

As a Manager, CSE (AI &amp; Cloud Support) at DigitalOcean, you will operate as a Support Manager leading a team of Customer Success Engineers (CSEs) supporting strategic customers across cloud infrastructure and emerging AI/ML workloads. Reporting to the Senior Manager of Customer Success Engineering, you will own day-to-day support operations, team performance, and customer experience outcomes.

This role is focused on driving operational excellence while building strong technical depth within the team across AI, GPUs, Kubernetes, Databases and core cloud offerings. You will play a key role in ensuring consistent support coverage, developing SMEs, and partnering cross-functionally with Product and Engineering to improve both customer experience and product quality.

The ideal candidate brings a deep understanding of cloud and AI/ML ecosystems, including machine learning operations (MLOps), and enterprise support, combined with a passion for innovation and automation, and a proven ability to lead teams in delivering &quot;white-glove&quot; experiences for strategic customers.<h2 class="jobSectionHeader"> 

What You'll Do:</h2>

Team Leadership &amp; Development
<ul>
<li>Lead, hire, train, mentor and develop a high-performing team of Customer Success Engineers (CSEs), driving accountability, performance, and career growth</li>
<li>Establish performance metrics (KPIs/SLAs) and conduct regular 1:1s, performance reviews, and career development planning.</li>
<li>Own end-to-end support operations, including queue management, escalations, and shift planning to ensure consistent 24x7 coverage.</li>
<li>Drive improvements in key support metrics such as CSAT, response times, resolution times, and overall support quality.</li>
<li>Build and strengthen technical expertise within the team across core areas such as Kubernetes (DOKS), Databases, Compute, and AI/ML workloads.</li> 
</ul>
Strategic Customer Support 
<ul>
<li>Act as the ultimate point of technical escalation for our largest, most strategic enterprise customers across Cloud and AI/ML workloads, stepping in to manage critical incidents and high-severity (Sev1/Sev2) issues.</li>
<li>Design and implement customized support plans, SLAs, and escalation pathways tailored to the needs of strategic accounts.</li>
<li>Partner closely with Technical Account Managers (TAMs), Growth Account Managers (GAM) to conduct Executive Business Reviews (EBRs) and ensure customers are maximizing the value of our Cloud and AI/ML products.</li>
<li>Proactively identify risks and opportunities within strategic accounts to improve customer experience, adoption, and retention.</li>
</ul>
Technical &amp; Cross-Functional Operations
<ul>
<li>Serve as the Voice of the Customer (VoC) to Product and Engineering teams, synthesizing support data to advocate for bug fixes, feature requests, and UX improvements.</li>
<li>Own and continuously improve escalation protocols between AI/ML Support and CloudOps, Infrastructure Engineering, and Product &mdash; including Jira escalation routing, Sev1 bridge management, and post-incident documentation.</li>
<li> Own the development and maintenance of SOPs, escalation runbooks, HVC support playbooks, and knowledge base content &mdash; treating documentation infrastructure as a core operational lever for team scalability.</li>
<li>Contribute to the vision for AI and automation within support&mdash;building intelligent tooling and driving the team toward an automation-first model to improve efficiency, scalability, and customer experience.</li>
<li>Foster a culture of continuous learning, ensuring the team stays ahead of evolving cloud technologies, AI/ML frameworks, and industry trends.</li>
</ul><h3 class="jobSectionHeader">Key Metrics:</h3>
<ul><li>Customer Satisfaction (CSAT) for strategic accounts</li>
<li>Time to Response and Resolution (TTR) for strategic customers</li>
<li>Tier 1 resolution rate vs. escalation rate</li>
<li>Time-to-escalation and engineering handoff SLA adherence</li>
<li>SLA adherence and escalation response times</li>
<li>Support productivity and quality (QA scores)</li>
<li>Post-incident documentation completion rate</li>
</ul><h2 class="jobSectionHeader">What You'll Add to DigitalOcean:</h2>
<ul><li>Experience: 5+ years of experience in Technical Support, Customer Success, or Technical Account Management within B2B SaaS, Cloud, or AI/ML environments ideally including experience supporting AI-native, high-growth companies with 24x7 production dependencies on GPU infrastructure.</li>
<li>Leadership: 2+ years of people management experience leading technical, customer-facing teams, preferably in a high-growth, post-acquisition, or rapidly scaling environment.</li>
<li>Technical Domain Expertise: Solid understanding of AI/ML concepts, including Generative AI, Large Language Models (LLMs), natural language processing (NLP), and MLOps. Deep familiarity with GPU infrastructure (NVIDIA H100/H200, bare metal GPU provisioning) and AI inference workloads is strongly preferred.</li>
<li>Coding/Integration: Proficiency in reading and debugging code (Python preferred) and troubleshooting RESTful APIs and cloud architecture.</li>
<li>Communication: Excellent verbal and written communication skills, with the ability to translate complex technical or AI concepts for diverse audiences OR to both highly technical engineers and non-technical business executives.</li>
<li>Problem-Solving: Proven ability to remain calm under pressure and de-escalate high-stakes situations with enterprise clients.</li>
</ul>
Preferred Qualifications:
<ul>
<li>Hands-on experience with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and AI toolchains (e.g., LangChain, Hugging Face).</li>
<li>Experience with major cloud platforms (AWS, Google Cloud, Azure) and their native AI/ML services.</li>
<li>Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related technical field.</li>
<li>ITIL or equivalent service management certification</li>
</ul><h2 class="jobSectionHeader">Compensation Range:</h2>
<ul><li>$125,000 - $153,000</li>
</ul>
<ul><li>This is a remote role</li></ul>

JR: 2026-7692

#LI-Remote<div><h2 class="jobSectionHeader"> 
Why You'll Like Working for DigitalOcean</h2>
<ul><li>We innovate with purpose. You'll be a part of a cutting-edge technology company with an upward trajectory, who are proud to simplify cloud and AI so builders can spend more time creating software that changes the world. As a member of the team, you will be a Shark who thinks big, bold, and scrappy, like an owner with a bias for action and a powerful sense of responsibility for customers, products, employees, and decisions.</li>
<li>We prioritize career development. At DO, you'll do the best work of your career. You will work with some of the smartest and most interesting people in the industry. We are a high-performance organization that will always challenge you to think big. Our organizational development team will provide you with resources to ensure you keep growing. We provide employees with reimbursement for relevant conferences, training, and education. All employees have access to LinkedIn Learning's 10,000+ courses to support their continued growth and development.</li>
<li>We care about your well-being. Regardless of your location, we will provide you with a competitive array of benefits to support you from our Employee Assistance Program to Local Employee Meetups to flexible time off policy, to name a few. While the philosophy around our benefits is the same worldwide, specific benefits may vary based on local regulations and preferences.</li>
<li>We reward our employees. The salary range for this position is based on market data, relevant years of experience, and skills. You may qualify for a bonus in addition to base salary; bonus amounts are determined based on company and individual performance. We also provide equity compensation to eligible employees, including equity grants upon hire and the option to participate in our Employee Stock Purchase Program.</li>
<li>DigitalOcean is an equal-opportunity employer. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service.</li>
</ul>
Application Limit: You may apply to a maximum of 3 positions within any 180-day period. This policy promotes better role-candidate matching and encourages thoughtful applications where your qualifications align most strongly.</div></div>

Manager, Customer Success Engineering

Genentech

<div><div><div>THE POSITION</div>
A healthier future. It&rsquo;s what drives us to innovate. To continuously advance science and ensure everyone has access to the healthcare they need today and for generations to come. Creating a world where we all have more time with the people we love. That&rsquo;s what makes us Roche.
Advances in AI, data and computational sciences are transforming drug discovery and development. Roche&rsquo;s Research and Early Development organizations at Genentech (gRED) and Pharma (pRED) have demonstrated how these technologies accelerate R&amp;D, leveraging data and novel computational models to drive impact. Seamless data sharing and access to models across gRED and pRED are essential to maximising these opportunities. The Computational Sciences Center of Excellence (CS CoE) is a strategic, unified group whose goal is to harness the transformative power of data and Artificial Intelligence (AI) to assist our scientists in both pRED and gRED to deliver more innovative and life-changing medicines for patients worldwide.
Within the CS CoE organisation, the Data and Digital Catalyst (DDC) organization leads the modernization of our computational and data ecosystems by integrating digital technologies across Research and Early Development to empower stakeholders, advance data-driven science and accelerate decision-making.
The Solutions team within the DDC Organization develops modernized and interconnected computational and data ecosystems. As a Site Reliability Engineer in the Solutions Engineering capability, you will work closely with our engineering colleagues to play a pivotal role in designing, implementing, and maintaining scalable, resilient, and supportable cloud-based platform solutions.
The focus will be on enabling research Application, Machine Learning (ML) workloads and HPC environments through automation, efficient resource management, and Infrastructure as Code (IaC) using tooling. As a member of the DDC team you will help mature the scalable platforms that help unlock the potential of our diverse scientific data, accelerating the discovery and development of life-changing treatments for patients. 
The Opportunity:
Infrastructure as Code (IaC) Design and Implementation<ul><li>
Architect and implement IaC solutions using tools like Terraform, Spacelift, or CloudFormation to provision and manage cloud infrastructure for ML and HPC workloads. Automate the deployment of scalable ML pipelines, HPC clusters, and supporting services across global regions.</li></ul>
Global Availability and Resiliency<ul><li>
Architect resilient and highly available solutions for ML and HPC workloads using cloud-native practices such as auto-scaling, load balancing, and failover mechanisms. Implement disaster recovery (DR) and business continuity plans for critical systems to ensure global operational integrity. Conduct chaos engineering experiments to validate system reliability and identify potential weaknesses.</li></ul>
Automation and Observability<ul><li>
Develop automation scripts and workflows to streamline infrastructure management, deployment, and scaling for ML and HPC use cases. Implement robust monitoring, logging, and alerting frameworks using tools like Prometheus, Grafana, Datadog, or ELK Stack to provide deep insights into system health and performance. Knowledge of AIOps incident management, processes and tooling.</li></ul>
Collaboration and Leadership<ul><li>
Provide technical leadership to a team of engineers, fostering a culture of collaboration, innovation, and continuous improvement. Partner with cross-functional teams to align infrastructure solutions with business objectives and ML/HPC workload requirements. Mentor and train junior engineers in IaC practices, ML, and HPC infrastructure design.</li></ul>
Cost Optimization and Governance<ul><li>
Monitor and optimize cloud infrastructure usage and costs for ML and HPC workloads. Ensure compliance with organizational security, governance, and regulatory policies in all IaC and cloud implementations.</li></ul>
Who You Are:<ul><li>
Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science or similar technical field, or equivalent experience and 7+ years of experience in software engineering Site Reliability Engineering (SRE).</li><li>
Proven expertise in supporting and deploying IaC solutions in cloud environments (AWS, Azure, or GCP) for ML and HPC workloads.</li><li>
Background in MLOps pipelines, including model versioning, CI/CD for ML, and feature store integration including experience with managed ML services (e.g., AWS SageMaker, Google AI Platform, or Azure ML).</li><li>
Deep understanding of cloud-native architectures, including autoscaling, serverless, and multi-region deployments.</li><li>
Technical Skills:<ul><li>
Advanced proficiency with IaC tools: Terraform, Pulumi, or CloudFormation.</li><li>
Expert in scripting and automation: Python, Bash, or Go.</li><li>
Strong understanding of GPU-accelerated computing (e.g., NVIDIA CUDA, TensorFlow) and HPC workload scaling.</li><li>
Knowledge of distributed systems, storage solutions, and data pipelines.</li><li>
Familiar with monitoring and observability tools: Prometheus, Grafana, Datadog, or similar.</li></ul></li><li>
Soft Skills:<ul><li>
Strong problem-solving skills, with a methodical approach to troubleshooting.</li><li>
Excellent communication, leadership, and mentoring abilities.</li><li>
Ability to work collaboratively across teams in a fast-paced, dynamic environment.</li></ul></li></ul>
Preferred Qualifications<ul><li>
Certifications in cloud platforms (e.g., AWS Certified Solutions Architect, GCP Professional Cloud Architect, or Azure Solutions Architect).</li><li>
Experience with distributed ML frameworks and data engineering pipelines (e.g., Horovod, TensorFlow Distributed, Apache Airflow, Apache Spark ).</li><li>
Experience with compliance frameworks (e.g., GDPR, SOC 2, ISO 27001).</li></ul>
Onsite presence, on our South San Francisco campus, is expected for at least 3 days a week.
Relocation benefits are not available for this job posting.
The expected salary range for this position based on the primary location of California is $162,600 - $302,000. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. A discretionary annual bonus may be available based on individual and Company performance. This position also qualifies for the benefits detailed at the link provided below.
Benefits
#LI-JD1
#ComputationCoE
Genentech is an equal opportunity employer. It is our policy and practice to employ, promote, and otherwise treat any and all employees and applicants on the basis of merit, qualifications, and competence. The company's policy prohibits unlawful discrimination, including but not limited to, discrimination on the basis of Protected Veteran status, individuals with disabilities status, and consistent with all federal, state, or local laws.
If you have a disability and need an accommodation in relation to the online application process, please contact us by completing this form Accommodations for Applicants.</div>
<div><div>JOB FACTS
</div><div><div><ul><div>Job Sub Category
Research Informatics
</div><div>Schedule
Full time
</div><div>Job Type
Regular
</div><div>Posted Date
Apr 13th 2026
</div><div>Job ID
202604-108676</div></ul></div></div></div></div>

Principal Site Reliability Engineer (Intelligent Automation)

THRAD

<div><h3 class="jobSectionHeader">The Role</h3>
We are looking for an Applied Scientist to own the models that decide which ad appears in which AI conversation, in real time, across millions of queries a day. Ad selection inside LLM chats is a genuinely new problem: no click history, no cookies, no page context. Just an evolving conversation and a few hundred milliseconds to make a decision that has to feel native to the user, relevant to the advertiser, and profitable for the publisher.
This is a research role with a production mandate. Your models go live, not into a paper. You will work directly with our CTO and the founding engineering team on the systems powering millions of ads a day across Fortune 500 campaigns.<h3 class="jobSectionHeader">
Core Tech Stack</h3><ul><li>
ML: PyTorch, embedding-based retrieval, modern transformer architectures</li><li>
Backend: Python (FastAPI), async serving, real-time inference</li><li>
Data: PostgreSQL for transactional, ClickHouse for analytics and event data</li><li>
Infra: AWS, Docker, CI/CD</li><li>
Plus: whatever the problem demands. We pick tools that ship.</li></ul><h3 class="jobSectionHeader">
Example Projects</h3><ul><li>
Design and train contextual ad relevance models for LLM conversations, from retrieval through ranking through creative selection.</li><li>
Build efficient algorithms for real-time bidding and auction mechanics, balancing publisher yield and advertiser ROAS.</li><li>
Productionize regression models to predict downstream conversion from early conversational signals.</li><li>
Apply privacy-preserving clustering methods to categorize conversational context and improve advertiser targeting without compromising user data.</li><li>
Run live experiments against real traffic on our publisher network. Iterate on CTR, ROAS, and conversation quality metrics.</li><li>
Own the evaluation framework for ad quality, relevance, and user experience across the marketplace.</li><li>
Read an ArXiv paper on Monday. Have a prototype running on Friday.</li></ul><h3 class="jobSectionHeader">
What You Will Do</h3><ul><li>
Own the research and shipping of the models powering ad selection across Thrad's network.</li><li>
Move research to production end to end: hypothesis, training, evaluation, deployment, monitoring.</li><li>
Collaborate directly with the CTO and co-founders to translate research direction into working systems, often within the same day.</li><li>
Partner with the Data and Product teams to design and analyze live experiments on real traffic.</li><li>
Contribute to system architecture decisions as we scale from millions to billions of daily ad impressions.</li><li>
Communicate findings clearly to both technical and non-technical audiences across the team.</li></ul><h3 class="jobSectionHeader">
Who You Are</h3>
Must-haves<ul><li>
2 to 5 years of applied ML experience, ideally in ads, search, recommendations, ranking, or NLP.</li><li>
An advanced degree in Computer Science, Mathematics, Statistics, Physics, Engineering, or a related field &mdash; or equivalent experience shipping production ML.</li><li>
Strong fundamentals in mathematical and statistical modeling. You know why your model works, not just that it does.</li><li>
Fluent in PyTorch, modern transformer architectures, and embedding-based retrieval.</li><li>
Strong Python, comfort with distributed training and inference.</li><li>
You have shipped ML to production at scale. Not just notebooks.</li><li>
You enjoy identifying and owning hard, open-ended problems. You form testable hypotheses and run them to ground.</li><li>
Based in San Francisco or willing to relocate. This is an in-person role at our SF HQ.</li></ul>
Nice-to-haves<ul><li>
Experience with RLHF, preference modeling, or LLM fine-tuning.</li><li>
Background in auction theory, bidding systems, real-time pricing, or yield optimization.</li><li>
Prior work at an ad-tech, search, recommendations, or marketplace company.</li><li>
Published research at NeurIPS, ICML, ACL, KDD, RecSys, or equivalent.</li><li>
Familiarity with ClickHouse or other column-oriented OLAP databases for analytical workloads.</li><li>
A relentless interest in the AI and LLM ecosystem and how advertising will evolve inside conversational interfaces.</li></ul><h3 class="jobSectionHeader">
Why Thrad</h3><ul><li>
First mover: We are defining a new category. Paid ads in AI is an inevitable market and we are building the infrastructure for it.</li><li>
Real traction: Fortune 500 clients, millions of daily impressions, and publisher partners across the AI web, all pre-seed.</li><li>
Research with teeth: Your work ships. Your models run in production against real traffic with real revenue on the line.</li><li>
Direct impact: Small founding team. Your decisions shape the models, the product, and the company.</li><li>
Ownership: Meaningful equity in a company at the intersection of two massive markets: AI and ad tech.</li><li>
Craft: A team that cares about shipping quality, not just quantity.</li></ul><h3 class="jobSectionHeader">
How to Apply</h3>
Send your CV and a brief note on what excites you about building at the intersection of AI and advertising. If you have published work, a GitHub, or models you have shipped that you are proud of, include them. We read every application.</div>

Applied Scientist

Apple

Apple Services Engineering (ASE) powers many AI features across App Store, Music, Video and more. We build deeply personal products with the goal of representing users around the globe authentically. We work continuously to avoid perpetuating systemic biases and maintain safe and trustworthy experiences across our AI tools and models. 
 
Description 
 
Our team, part of Apple Services Engineering, is looking for an ML Research Engineer to lead the design and continuous development of automated safety benchmarking methodologies. In this role, you will investigate how media-related agents behave, develop rigorous evaluation frameworks and techniques, and establish scientific standards for assessing risks they pose and safety performance. This role supports the development of scalable evaluation techniques that ensure our engineers have the right tools to assess candidate models and product features for responsible and safe performance. 
 
The capabilities you build will allow for the generation of benchmark datasets and evaluation methodologies for model and application outputs, at scale, to enable engineering teams to translate safety insights into actionable engineering and product improvements. This role blends deep technical expertise with strong analytical judgment to develop tools and capabilities for assessing and improving the behavior of advanced AI/ML models. You will work cross-functionally with Engineering and Project Managers, Product, and Governance teams to develop a suite of technologies to ensure that AI experiences are reliable, safe, and aligned with human expectations. 
 
The successful candidate will take a proactive approach to working independently and collaboratively on a wide range of projects. In this role, you will work alongside a small but impactful team, collaborating with ML and data scientists, software developers, project managers, and other teams at Apple to understand requirements and translate them into scalable, reliable, and efficient evaluation frameworks.&quot;,&quot;responsibilities&quot;:&quot;Design scientifically-grounded benchmarking methodologies covering multiple dimensions of responsibility and safety across several media and application marketplace use cases 
 
Develop automated evaluation pipelines that collect, automatically judge, and analyze model outputs with respect to safety policies, at scale 
 
Create and curate datasets, tasks, and feature usage scenarios that represent realistic and adversarial use cases across multiple languages, markets, and domains 
 
Define and validate new metrics for complex phenomena such as multi-turn agentic interaction patterns 
 
Apply statistical rigor and reproducibility to above mentioned objectives 
 
Work closely with engineering and research teams to translate experimental findings into actionable model improvements and safety mitigations 
 
Publish internal reports and external papers 
 
Monitor evolving industry practices and academic work to ensure benchmarks remain relevant 
 
Preferred Qualifications 
 
Publications in AI/ML evaluation or related fields 
 
Experience with automated testing frameworks 
 
Experience constructing human-in-the-loop or multi-turn evaluation setups 
 
Intermediate or Advanced Proficiency in Swift 
 
Familiarity with RAG systems, reinforcement learning, agentic architectures, and model fine-tuning 
 
Expertise in designing annotation guidelines and validation instruments and techniques 
 
Background in human factors, social science, and/or safety assessment methodologies 
 
Minimum Qualifications 
 
Advanced degree (MS or PhD) in Computer Science, Software Engineering, or equivalent research/work experience 
 
1+ years of work experience either as a postdoc or in the industry 
 
Strong research background in empirical evaluation, experimental design, or benchmarking 
 
Strong proficiency in Python (pandas, NumPy, Jupyter, PyTorch, etc.) 
 
Deep familiarity with software engineering workflows and developer tools 
 
Experience working with or evaluating AI/ML models, preferably LLMs or program synthesis systems 
 
Strong analytical and communication skills, including the ability to write clear reports 
 
Technical Skills: 
 
Proficiency in Python (pandas, NumPy, Jupyter, PyTorch, etc.). 
 
Experience working with large datasets, annotation tools, and model evaluation pipelines 
 
Familiarity with evaluations specific to responsible AI and safety, hallucination detection, and/or model alignment concerns 
 
Ability to design taxonomies, categorization schemes, and structured labeling frameworks 
 
Analytical Strength: Ability to interpret unstructured data (text, transcripts, user sessions) and derive meaningful insights 
 
Communication: Strong ability to stitch together qualitative and quantitative insights into actionable guidance; strong ability to communicate complex architectures and systems to a variety of stakeholders 
 
Education in Data Science, Linguistics, Cognitive Science, HCI, Psychology, Social Science, or a related field 
 
Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant . 
 
Pay &amp; Benefits 
 
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $147,400 and $272,100, and your base pay will depend on your skills, qualifications, experience, and location. 
 
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits. 
 
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

ML Safety Engineer, ASE

Encord

<div>
 
 About Us
 
 
 
 At Encord, we're building the AI infrastructure of the future. The biggest challenge AI companies face today is not half as glamorous as the outside world may think: it's all about data quality. In fact, the success of any AI application today relies on the quality of a model's training data &mdash; and for 95% of teams, this essential step is both the most costly, and the most time-consuming, in getting their product to market.
 
 
 As ex-computer scientists, physicists, and quants, we felt first-hand how the lack of tools to prepare quality training data was impeding the progress of building AI. AI today is what the early days of computing or the internet were like, where the potential of the technology is clear, but the tools and processes surrounding it are still primitive, preventing the next generation of applications. This is why we started Encord.
 
 
 We are a talented and ambitious team of 90+, working at the cutting edge of computer vision and deep learning, backed by top investors, including CRV and Y Combinator, leading industry executives like Luc Vincent, former VP of AI at Meta, and other top Bay Area leaders in AI. We are one the fastest growing companies in our space, and consistently rated as the best tool in the market by our customers. We have big plans ahead and are looking for a Machine Learning Engineer to join us our ML team.
 
 
 
 
 The Role
 
 
 
 
 We Are Looking For An Experienced Machine Learning Engineer To Help Us Conduct Research On The State Of The Art Of Computer Vision And Solve Multifaceted Algorithmic Problems. You Will
 
 
 <ul>
 <li>
 Experiment with and adapt latest ML technologies to fit into existing tech stack
 </li>
 <li>
 Solve idiosyncratic statistical, geometric, and engineering problems
 </li>
 <li>
 Work closely with a full stack tech team to assist implementation of research solutions into the product
 </li>
 <li>
 Contribute to hiring additional talent to our rapidly growing team
 
 
 </li>
 </ul>
 The role will be exposed to a broad tech stack (e.g. ReactJS, Python, REST &amp; GraphQL, OpenCV, PyTorch, GCP, AWS &amp; CUDA, Kubernetes) and the cutting edge of computer vision and deep learning.
 
 
 
 Qualifications
 
 
 
 The right candidate will have a proven track record of relevant publications and previous experience managing applied research teams. Requirements for the role include:
 
 
 <ul>
 <li>
 Passion for solving ML problems
 </li>
 <li>
 Strong experience in Python and machine learning libraries such as OpenCV, PyTorch, TensorFlow, Fast.ai , and Keras
 </li>
 <li>
 Strong experience in mathematical programming, algorithmic problem solving, and applied machine learning
 
 
 </li>
 </ul>
 
 
 What We Offer
 
 
 
 
 <ul>
 <li>
 Competitive salary and equity in a hyper growth startup.
 </li>
 <li>
 Real opportunities to grow. We&rsquo;re growing insanely fast and you&rsquo;ll have all the opportunities for growth that you can handle.
 </li>
 <li>
 Strong in person culture. We're 3-5 days a week in our newly-launched loft office in North Beach.
 </li>
 <li>
 Flexible PTO to recharge.
 </li>
 <li>
 Annual learning and development budget.
 </li>
 <li>
 18 paid vacation days + federal holidays.
 </li>
 <li>
 Lots of opportunities for travel (all around the US, to London &amp; Europe).
 </li>
 <li>
 Bi-annual off-sites and monthly socials.
 </li>
 <li>
 Health, dental and vision insurance.
 
 
 </li>
 </ul>
 Encord offers a unique opportunity to be part of a startup with a clear mission and vision. You will get to explore and build services enterprise AI use cases across many different industry verticals such as healthcare, surveillance, retail, agriculture, and many more.
 
 
 Our work is at the cutting edge of computer vision and deep learning, which also includes working on solving unsolved problems within those fields.
 
 
 We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
</div>

Machine Learning Engineer, Physical AI

Planet

<div>
 
 Welcome to Planet. We believe in using space to help life on Earth.
 
 
 
 Planet designs, builds, and operates the largest constellation of imaging satellites in history. This constellation delivers an unprecedented dataset of empirical information via a revolutionary cloud-based platform to authoritative figures in commercial, environmental, and humanitarian sectors. We are both a space company and data company all rolled into one.
 
 
 Customers and users across the globe use Planet's data to develop new technologies, drive revenue, power research, and solve our world&rsquo;s toughest obstacles.
 
 
 As we control every component of hardware design, manufacturing, data processing, and software engineering, our office is a truly inspiring mix of experts from a variety of domains.
 
 
 We have a people-centric approach toward culture and community and we strive to iterate in a way that puts our team members first and prepares our company for growth. Join Planet and be a part of our mission to change the way people see the world.
 
 
 Planet is a global company with employees working remotely world wide and joining us from offices in San Francisco, Washington DC, Germany, Austria, Slovenia, and The Netherlands.
 
 
 
 About The Role
 
 
 
 Planet is in the middle of its most ambitious hiring year &mdash; onboarding hundreds of new team members across San Francisco, Washington DC, Berlin, and remote locations worldwide. Behind every hire is a set of operational workflows that need to run flawlessly: requisition approvals, interview coordination, offer processing, compliance checks, onboarding handoffs, and program management for specialized hiring tracks like our global intern program. The Talent Operations team is responsible for all of it &mdash; and we are looking for a Talent Operations Partner to step in and own critical pieces of that work.
 
 
 As Talent Operations Partner, you will serve two primary functions. First, you will own and manage Planet&rsquo;s intern and early talent program end-to-end &mdash; from requisition management and candidate coordination through onboarding, program programming, and offboarding. This is a visible, cross-functional program that touches hiring managers across every department and gives early-career candidates their first experience of Planet. Second, you will contribute directly to the Talent Operations roadmap, a portfolio of process improvement and systems projects that are actively reshaping how Planet manages the hiring lifecycle.
 
 
 This role sits at the intersection of program management, process design, and recruiting operations. You will need to be equally comfortable running the logistics of an intern cohort and digging into ATS workflow configuration. The common thread is operational excellence &mdash; you care about whether things run smoothly, you notice when they don&rsquo;t, and you fix them before someone has to ask.
 
 
 You will report to the Senior Manager, Talent Operations and work as part of a team that includes Talent Operations, Recruiting Coordinators, and a Working Student. Together, this team ensures that Planet&rsquo;s recruiting infrastructure scales in step with the company&rsquo;s growth. This is a full-time, hybrid role which will require you to work from our San Francisco office 3 days per week.
 
 
 
 Impact You&rsquo;ll Own
 
 
 
 <ul>
 <li>
 Own and manage Planet&rsquo;s intern and early talent program end-to-end &mdash; including requisition intake, candidate coordination, offer processing, onboarding logistics, program events and content, manager enablement, and offboarding workflows.
 </li>
 <li>
 Serve as the primary point of contact for hiring managers and interns throughout the program lifecycle &mdash; ensuring hiring managers know what to expect and interns have a structured, high-quality experience from day one through final presentations.
 </li>
 <li>
 Partner with Talent Acquisition Partners to coordinate intern recruiting timelines, track pipeline progress across departments, and ensure all intern offers are processed accurately and on time.
 </li>
 <li>
 Contribute to the Talent Operations roadmap by owning or supporting key projects, with an immediate focus on the headcount approval workflow redesign &mdash; documenting the current process, mapping the ideal state, building automated workflows, and creating stakeholder-facing documentation.
 </li>
 <li>
 Maintain and improve ATS (Greenhouse) workflows and configurations related to intern hiring, offer approvals, and operational reporting &mdash; ensuring data integrity and process consistency across all hiring tracks.
 </li>
 <li>
 Support SLA monitoring and compliance by tracking scorecard submission timelines, offer approval turnaround, and other operational metrics &mdash; flagging risks early and following up with stakeholders to keep processes on track.
 </li>
 <li>
 Create and maintain process documentation in the talent operations knowledge base &mdash; ensuring that intern program playbooks, approval workflows, and operational procedures are current, clear, and accessible to the recruiting team and hiring managers.
 </li>
 <li>
 Coordinate cross-functionally with People Operations, People Business Partners, and the L&amp;D team on intern onboarding, compliance requirements, and program content that connects interns to Planet&rsquo;s mission and culture.
 </li>
 <li>
 Support additional recruiting operations projects as capacity allows &mdash; including AI tool deployment, global process standardization, interview process documentation, and reporting enhancements.
 </li>
 <li>
 Identify operational inefficiencies and propose improvements.
 
 
 </li>
 </ul>
 
 What You Bring
 
 
 
 <ul>
 <li>
 Bachelor&rsquo;s Degree
 </li>
 <li>
 2+ years of professional experience in recruiting operations, talent operations, HR operations, or program coordination at a technology company or similarly dynamic environment.
 </li>
 <li>
 Experience managing or supporting an intern, early talent, or campus recruiting program &mdash; including the logistics, stakeholder communication, and coordination required to run a multi-department cohort program smoothly.
 </li>
 <li>
 Familiarity with applicant tracking systems, including data entry, workflow navigation, reporting, and basic configuration.
 </li>
 <li>
 Effective project management skills: you can manage multiple concurrent workstreams, track deliverables and deadlines, and communicate progress to stakeholders without being micromanaged.
 </li>
 <li>
 Effective organizational skills and attention to detail &mdash; recruiting operations is a high-volume, high-precision function where small errors in offer letters, approval chains, or compliance documentation create real downstream problems.
 </li>
 <li>
 Clear written and verbal communication skills &mdash; you will draft process documentation, communicate with hiring managers across the company, and correspond with intern candidates who are forming their first impression of Planet.
 </li>
 <li>
 A process improvement mindset &mdash; you don&rsquo;t just run the process, you question whether it&rsquo;s the right process. You look for bottlenecks, redundancies, and manual steps that could be automated or eliminated.
 </li>
 <li>
 Comfort working in a team that is actively building and improving its own infrastructure &mdash; the Talent Operations function is evolving, and you need to be energized by contributing to that build rather than expecting a fully mature playbook.
 
 
 </li>
 </ul>
 
 What Makes You Stand Out
 
 
 
 <ul>
 <li>
 Experience with headcount planning or approval workflow tools and an understanding of how requisition approval processes connect to HRIS and finance systems.
 </li>
 <li>
 Previous experience building or redesigning an operational process from scratch &mdash; documenting current state, proposing improvements, and implementing changes with measurable results.
 </li>
 <li>
 Familiarity with intern program best practices including cohort programming, manager enablement, structured feedback cycles, and conversion-to-hire tracking.
 </li>
 <li>
 Experience working with global hiring operations, particularly coordinating intern or early talent programs that span multiple offices or geographies.
 </li>
 <li>
 Exposure to recruiting metrics and reporting &mdash; you have pulled reports from an ATS, built tracking spreadsheets, or contributed to operational dashboards that inform team decision-making.
 </li>
 <li>
 Experience with Greenhouse, the applicant tracking system.
 
 
 </li>
 </ul>
 
 Application Deadline
 
 
 
 June 6, 2026 by 11:59pm PT
 
 
 
 Benefits While Working At Planet
 
 
 
 
 These offerings are dependent on employment type and geographical location, based upon applicable law or company policy.
 
 
 
 <ul>
 <li>
 Comprehensive Medical, Dental, and Vision plans
 </li>
 <li>
 Health Savings Account (HSA) with a company contribution
 </li>
 <li>
 Generous Paid Time Off in addition to holidays and company-wide days off
 </li>
 <li>
 16 Weeks of Paid Parental Leave
 </li>
 <li>
 Wellness Program and Employee Assistance Program (EAP)
 </li>
 <li>
 Home Office Reimbursement
 </li>
 <li>
 Monthly Phone and Internet Reimbursement
 </li>
 <li>
 Tuition Reimbursement and access to LinkedIn Learning
 </li>
 <li>
 Equity
 </li>
 <li>
 Commuter Benefits (if local to an office)
 </li>
 <li>
 Volunteering Paid Time Off
 
 
 </li>
 </ul>
 
 Compensation
 
 
 
 The US base salary range for this full-time position at the commencement of employment is listed below. Additionally, this role might be eligible for discretionary short-term and long-term incentives (bonus and equity). The final salary range is determined by job related experience, skills and location. The range displays our typical hiring range for new hire salaries in US locations only. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
 
 
 San Francisco Salary Range
 
 
 $80,200&mdash;$100,200 USD
 
 
 
 San Francisco Fair Chance Ordinance
 
 
 
 Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
 
 
 
 Why We Care So Much About Belonging.
 
 
 
 We&rsquo;re dedicated to helping the whole Planet, and to do that we must strive to represent all of it within each of our offices and on all of our teams. That&rsquo;s why Planet is guided by an ultimate north star of Belonging&mdash;dreaming big as we approach our ongoing work. If this job intrigues you, but you&rsquo;re thinking you might not have all the qualifications, please... do apply! At Planet, we are looking for well-rounded people from around the world who can contribute to more ways than just what is listed in this job description. We don&rsquo;t just fill positions, we aspire to fulfill people&rsquo;s careers, most excited about folks who are motivated by our underlying humanitarian efforts. We are a few orbits around the sun before we get to where we want to be, so we hope you&rsquo;re excited to come along for the ride.
 
 
 
 EEO Statement
 
 
 
 Planet is committed to building a community where everyone belongs and we invite people from all backgrounds to apply. Planet is an equal opportunity employer, and committed to providing employment opportunities regardless of race, religious creed, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, pregnancy, childbirth and breastfeeding, age, sexual orientation, military or veteran status, or any other protected classification, in accordance with applicable federal, state, and local laws. Know Your Rights.
 
 
 
 Accommodations
 
 
 
 Planet is an inclusive community and we know that everyone has their own needs. If you have a disability or special need that requires accommodation during the hiring process, please reach out to accommodations@planet.com or contact your recruiter with your request. Your message will be confidential and we will be happy to assist you.
 
 
 
 Privacy Policy
 
 : By clicking "Apply Now" at the top of this job posting, I acknowledge that I have read the Planet Data Privacy Notice for California Staff Members and Applicants, and hereby consent to the collection, processing, use, and storage of my personal information as described therein.
 
 
 
 Privacy Policy (European Applicants):
 
 By clicking "Apply Now" at the top of this job posting, I acknowledge that I have read the Candidate Privacy Notice GDPR Planet Labs Europe, and hereby consent to the collection, processing, use, and storage of my personal information as described therein.
 
 
 
 AI in Our Interviewing Process
 
 : Planet is committed to providing an exceptional interview experience for all candidates. We are currently exploring AI interviewing technologies to better focus on candidates and less on trying to capture notes. As such, with the candidate's consent, select interviews may be recorded and include a Planet AI Notetaker for transcription and summarization purposes. Should an interview involve use of AI interview technologies, the candidate will receive notification and have the ability to opt out both in advance and/or real-time. Opting out will not affect one's candidacy.
 
 
 
 Candidate AI Policy
 
 : Planet embraces Artificial Intelligence (AI) tools, and we encourage its responsible use. We understand that candidates may use various resources, including AI tools, to
 
 prepare
 
 for interviews and assessments. However,
 
 during any live interview stage or when actively completing assessments for this position, the use of AI tools&mdash;e.g. Large Language Models (LLMs), deep fake technology, etc.&mdash;is strictly prohibited unless explicitly prompted by an interviewer or assessment instructions
 
 . If you are unsure about acceptable use, please contact your recruiter for clarification. If an AI tool or similar technology is desired as an accommodation, please contact accommodations@planet.com with your request for assistance. Your message will be confidential, and we will be happy to assist you. Violation of this policy may result in disqualification of your application.
</div>

Talent Operations Partner

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