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senior ml data engineer ise

Apple

Do you believe Machine Learning and AI can change the world? We truly believe it can! We are the ML Data Team of the Intelligent System Experience (ISE) group at Apple. We are responsible for building high quality datasets at scale. Every year, our team produces datasets used in the training of ML and AI-centric features for many Apple products, including iPhone, iPad, Mac, Apple Watch and even AirPods. Our work is used in very visible and critical features, from the wallpaper on your iPhone Lock Screen, to your personalized stickers on iMessage, to Apple Intelligence features such as on-device Genmoji and other visual generative models, to the models that highlight the faces and memories of your loved ones in your Photos app. 
 
We&rsquo;re looking for an exceptional software &amp; data engineer who is passionate about Apple products and values, who has a passion for data operations at scale, is comfortable in a fast paced environment and who is committed to the hard work necessary to continuously improve our ML data pipelines. 
 
We invite you to join us at this exciting time. Grow fast and positively impact multiple critical features from your first day at Apple! 
 
Description 
 
Our team focuses on acquiring, synthesizing, annotating, and ensuring the quality of ML data, driving numerous features in collaboration with R&amp;D teams in Apple&rsquo;s SWE organization.&quot;,&quot;responsibilities&quot;:&quot;Operate and maintain data pipelines to effectively process up to billion-scale multimodal data 
 
Find creative ways to automate workflows or build self service tools, implement a variety of data tools for visualization, compliance and ML enrichment 
 
Help bring frontier generative model and agentic capabilities in our pipelines to further optimize our data operations ; and production-ize synthetic data workflows including orchestration and lineage 
 
Identify opportunities for improvement of internal infrastructure offerings, and influence roadmaps of partner teams to build or improve key components we rely on 
 
Preferred Qualifications 
 
Proven experience designing, automating and scaling large data pipelines (petabyte scale desired) using state of the art processing technologies (eg familiarity with Iceberg, BigQuery, Airflow, Spark, etc.) 
 
Expertise in Python, Go or another modern programming language; you are expected to produce high quality, efficient code and participate in code reviews 
 
Strong ability to design and lead a technical roadmap, work with cross functional teams and a diversity of profiles, proven capacity to influence and build alignment 
 
Demonstrated prior experience in delivering large scale multimodal datasets for foundation models 
 
Self-starter, able to handle ambiguity, comfortable in a fast-pace environment, identify risks, mitigate, and autonomously find the right people and tools to get the job done 
 
Proven track record of mentoring and growing engineers, lift the level of software engineering excellence in a ML data ops team typically focused on short term deliveries 
 
Minimum Qualifications 
 
7+ years of industry experience as a data engineer serving various ML applications (vision domain preferred) 
 
Bachelor's degree in Computer Science or 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 $181,100 and $318,400, 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.

Senior ML Data Engineer, ISE

LATENT

<div><h1 class="jobSectionHeader">Machine Learning Engineer</h1><h2 class="jobSectionHeader">
About Latent Health</h2>
Healthcare today is only truly personalized for two groups: those with wealth and access, and those with physicians in their immediate family.
For everyone else, care is fragmented and impersonal.
Medical history is scattered across systems that don&rsquo;t communicate. Physicians have minutes to understand decades of context. And when something goes wrong, patients are left with tools that understand medicine broadly&mdash;but not the individual.
We believe this can be fundamentally rebuilt.
At Latent Health, we are building systems that understand both:<ul><li>
the population (clinical knowledge at scale)</li><li> 
and the individual (longitudinal patient history)</li></ul> 
Our models are designed to answer complex clinical questions with patient-specific context and verifiable reasoning.
Our dataset represents one of the most clinically diverse populations in the United States, including patients with chronic illness and complex disease. Each patient record contains extraordinary depth.<h2 class="jobSectionHeader">
ML at Latent Health</h2>
The Machine Learning team is responsible for building systems that run in real clinical workflows.
We work on:<ul><li>
Verifiable reinforcement learning at scale</li><li> 
Mid-training and post-training of foundation models</li><li> 
Novel objectives derived from longitudinal patient data</li></ul> 
We are a small group of researchers and engineers focused on pushing the frontier while shipping real systems into production.
We are a small team and expect engineers to take ownership of critical systems, not components.<h2 class="jobSectionHeader">
The Role</h2>
As a Machine Learning Engineer, you will own the design, development, and operation of production-grade ML systems that run in real clinical workflows.
You will drive systems from ambiguous problem definition through to reliable production deployment, setting technical direction along the way.
We are primarily hiring for senior and staff-level engineers who are comfortable owning critical systems end-to-end.
This role involves owning systems that directly impact real patient outcomes.<h2 class="jobSectionHeader">
What You&rsquo;ll Do</h2><ul><li>
Own end-to-end ML systems, including architecture, data, modeling, evaluation, and production infrastructure</li><li> 
Train and fine-tune large language models (LLMs) for:<ul><li> 
Clinical reasoning</li><li> 
Medical question answering</li><li> 
Evidence-grounded generation</li></ul></li><li> 
Make and own tradeoffs across accuracy, latency, cost, and safety in high-stakes production environments</li><li> 
Develop evaluation frameworks to ensure model safety and clinical validity</li><li> 
Integrate ML systems into product workflows and patient-facing applications</li><li> 
Monitor system performance in production and iterate based on real-world usage and feedback</li><li> 
Define what &ldquo;correct&rdquo; means in ambiguous clinical workflows in collaboration with engineers and clinicians</li></ul><h2 class="jobSectionHeader"> 
What We&rsquo;re Looking For</h2><ul><li>
Strong foundation in machine learning and software engineering</li><li> 
Track record of building and owning ML systems in production where performance, reliability, or correctness materially mattered</li><li> 
Experience driving ambiguous ML problems from 0 1, including problem formulation, model design, and productionization</li><li> 
Hands-on experience with PyTorch or similar frameworks</li><li> 
Ability to operate independently in high-ambiguity environments with minimal guidance</li><li> 
Strong product and engineering judgment &mdash; you know when to use ML, when not to, and how to scope problems accordingly</li><li> 
Comfort working in a fast-moving, early-stage environment</li><li> 
Experience working on systems where decisions have real-world consequences (e.g., healthcare, finance, infrastructure)</li></ul><h2 class="jobSectionHeader"> 
Nice to Have</h2><ul><li>
Experience deploying LLMs in production environments</li><li> 
Experience building distributed systems or large-scale data pipelines</li><li> 
Experience working with clinical, biomedical, or other regulated datasets</li></ul><h2 class="jobSectionHeader"> 
Why Join Latent Health</h2><ul><li>
Work on high-stakes problems with real impact on patient care</li><li> 
Build systems that define how AI is trusted in clinical decision-making</li><li> 
Significant ownership in a small, high-caliber team</li><li> 
Competitive compensation and meaningful equity</li></ul><h2 class="jobSectionHeader"> 
Location</h2>
We are based in San Francisco and work together in person.
We spend most of the week in the office and prioritize candidates who are excited to work this way.<h2 class="jobSectionHeader">
Compensation</h2><ul><li>
Base salary: $225,000 &ndash; $300,000+</li><li> 
Meaningful equity in an early-stage, Series A company</li></ul><h2 class="jobSectionHeader"> 
Closing</h2>
If you&rsquo;re interested in building systems that bring truly personalized healthcare to millions of patients, we&rsquo;d love to talk.</div>

Machine Learning Engineer

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)

Hex Technologies

SF or NYC
 About the role
 
Hex is an AI-powered platform for modern Data Science and Data Analytics workflows. AI Research Engineers at Hex partner with product teams to build industry-leading AI experiences such as the Notebook Agent. In the process, AI Research Engineers will run experiments, fine-tune models, deploy AI infrastructure, and build and maintain experimentation tooling.
 
As an AI Research Engineer partnered with Hex's product teams, your focus will be driving Hex's context engine and pushing forward the capabilities of our Notebook Agent. Built for people doing the most complex and impactful data work in an organization, the Notebook Agent is a highly technical data copilot that writes SQL and python, builds beautiful reports and visualizations, and collaborates with the analyst on new avenues of inquiry. Whether its pushing the frontiers of SQL generation quality or using LLMs to evaluate agent threads, your job will be to enable the data teams that work in Hex to build out highly accurate and curated data experiences for their stakeholders, unlocking the power of data driven decisions for the rest of the business.
 
If you’re a strong builder excited to bring these capabilities to thousands of users, come do it on the best Data Science platform around, with the best user context.
 
We are looking for a senior engineer (from an AI Eng, SWE or MLE background) eager to rapidly expand our capabilities in several greenfield applications. As one of our team’s early hires, you will work on a variety of initiatives including:
 
Building features and experiences from 0 to 1
 Partnering on determining the architecture and stack for our AI-enabled capabilities
 Shipping product experiences that fundamentally change the way that Data Scientists and Analysts operate
 Working at the cutting edge of production AI applications
 
This type of ownership will provide incredible opportunities for personal and professional growth. You will have ample technical and team leadership opportunities, depending on your interests.
 
We aim to deliver AI capabilities that radically accelerate and augment the data science workflow – we’ve got an exciting roadmap and are willing to share more details during the process. We’re particularly interested in candidates who bring inspired excitement about what is possible in this space.
 
We'd love to hear from you if you have: 
Experience getting AI/ML capabilities into production and serving real users – we’re not currently looking for new grad folks.
 A lot of enthusiasm for applications of AI to real business problems.
 Understanding of core MLOps/SW Architecture concepts for modern ML-based applications.
 Ideal candidates are strong on Infra aspects of MLOps as well.
 You’re comfortable working in both Python & JS/TS – it’s okay if you’re only strong in one, but openness to both is important.
 Experimentalist mindset – understanding that quick iteration, and discerning critical thinking, can yield rapid progress on new challenges.
 Interest in the data space, and a love of shipping great products and building tools that empower end users to do more.
 Experience maintaining a high quality bar for design, correctness, and testing.
 Our stack
 
Our product is a web-based notebook and app authoring platform. Our frontend is built with Typescript and React, using a combination of Apollo GraphQL and Redux for managing application state and data. On the backend, we also use Typescript to power an Express/Apollo GraphQL server that interacts with Postgres, Redis, and Kubernetes to manage our database and Python kernels. Our backend is tightly integrated with our infrastructure and CI/CD, where we use a combination of Terraform, Helm, and AWS to deploy and maintain our stack.
 
This is a full-time role based out of either our San Francisco or New York office. Employees are encouraged to come into the office at least twice a week, with Tuesdays and Thursdays being our main in-person days.
 
In addition to our unique culture, Hex proudly offers a competitive total rewards package, including but not limited to, market-benched salary & equity, comprehensive health benefits, and flexible paid time off.
 
The salary range for this role is: $214,000 - $285,000
 
The salary range shown may be a reflection of additional factors such as geographical location and skill ranges/levels we’re open to. Placement in the salary range will be decided upon completion of the interview process, taking into account factors like leaving room for growth, internal fairness & parity, your demonstrated skills, and the depth of your experience. Our Recruiting team will be able to provide more details during the interview process.
 
By submitting an application the candidate consents to the use of their personal information in accordance with the Hex Privacy policy: https://learn.hex.tech/docs/trust/privacy-policy.
 
About Hex
 
Hex is growing our team of builders on a mission to make everyone a data person. Our platform solves key pain points with today's data and analytics tooling, and empowers anyone to explore data using natural language, with or without code, on trusted context. Thousands of customers like Ramp, Figma, Stubhub, Anthropic, and Gamma love Hex for our beautiful UI, agentic superpowers, and boundless flexibility.
 
Hex has raised over $100M from leading investors like Sequoia, a16z, Snowflake and Amplify. With office hubs in San Francisco and New York, as well as remote team members throughout the US and UK, we're a high-agency team that ships fast and has fun doing it. Check out our Intro to Hex video and employee handbook to learn more about what it's like to work at Hex.

AI Research Engineer

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

LendingClub

<div><h3 class="jobSectionHeader">Current Employees of LendingClub: Please apply via your internal Workday Account</h3>
LendingClub Corporation (NYSE: LC) is the parent company of LendingClub Bank, National Association, Member FDIC. We are the leading digital marketplace bank in the U.S., having helped our nearly 5 million members secure over $90 billion in loans to refinance high-cost debt and achieve their financial goals. Members today have mobile-first access to a growing range of products and services designed to work seamlessly together to deliver value in new ways. Everyone deserves a better financial future, and our team is committed to making that a reality. Join the Club!<h2 class="jobSectionHeader">
About the Role</h2>
Our mission at LendingClub is to empower those who strive to achieve better financial health. The Data and Analytics team plays a crucial role in achieving our mission. 
 
We are seeking a Principal Data Engineer to lead the design and evolution of data systems that power batch processing, real-time streaming, pipeline orchestration, data lake management, data cataloging, and machine learning workflows. This role has a strong emphasis on enabling and scaling machine learning and analytics use cases, including feature engineering, model training, and inference data pipelines. 
 
In this role, you will apply deep technical expertise, architectural thinking, and hands-on development to solve complex big data and ML platform challenges. You will partner closely with Data Science, Product, and Platform teams to build reliable, scalable, and cost-efficient data foundations that support both analytics and production machine learning systems.<h2 class="jobSectionHeader">
What You'll Do</h2><ul><li>
Design, build, and own large-scale data and ML data pipelines that integrate directly with LendingClub&rsquo;s products and external vendors</li><li>
Design and operate data platforms where autonomous coding agents help maintain pipelines, schemas, and tests, while you own architecture and guardrails.</li><li>
Lead the architecture and implementation of MLOps including feature stores, training datasets, batch and real-time inference pipelines</li><li>
Work with modern data technologies such as Hadoop, Spark, DBT, Dagster/Airflow, Atlan, and modern data platforms like Databricks and Snowflake, across the AWS cloud stack</li><li>
Partner with Data Scientists to productionize ML workflows and ensure data reliability, reproducibility, and performance</li><li>
Identify, design, and implement automation of manual processes, optimizing data delivery, improving system reliability, and reducing cloud costs</li><li>
Implement processes and systems to monitor Data Quality, Observability, Governance, Lineage, and ML data consistency</li><li>
Define policies, workflows, and quality gates for using AI agents in production data systems.</li><li>
Provide technical leadership and mentorship, influencing data engineering standards, best practices, and long-term platform strategy</li><li>
Coach teams on decomposing work, supervising agents, and validating AI-generated changes.</li><li>
Support operations to manage the production environment and lead root cause analysis (RCA) for complex data and ML pipeline issues</li><li>
Write unit and integration tests, advocate for test-driven development, contribute to engineering documentation and design reviews<div>
What You'll Do</div></li><li>
Build and govern data pipelines that support model training, feature generation, retrieval-augmented workflows, or AI-driven analytics</li><li>
Enable secure, production-grade access patterns for AI use cases through curated datasets, semantic layers, metadata, monitoring, and policy-aware data services</li><li><div> 
About You</div></li><li>
You are highly fluent in modern AI data ecosystems and understand how to build the foundations required for AI and GenAI applications to succeed in production</li><li>
You know how data quality, lineage, retrieval patterns, feature access, vector storage, governance, and real-time or batch serving architectures influence AI system performance and trustworthiness</li><li>
You can evaluate tradeoffs, guide platform direction, and enable teams to build AI-ready data products responsibly and at scale</li></ul><h2 class="jobSectionHeader">
About You</h2><ul><li>
8+ years of data engineering experience with deep hands-on experience with distributed data systems including Hadoop, Spark, Hive, DBT, and Airflow/Dagster</li><li>
Bachelor&rsquo;s degree in computer science or a related field, or equivalent work experience</li><li>
5+ years of production-quality Python experience, building and maintaining large-scale data pipelines</li><li>
Strong experience building machine learning use cases through data engineering (e.g., feature engineering pipelines, training/inference data flows)</li><li>
Experience working with public cloud platforms, preferably AWS</li><li>
Experience with Databricks and/or Snowflake in production environments</li><li>
Strong working knowledge of Git, JIRA, Jenkins, shell scripting</li><li>
Familiarity with Agile methodologies, test-driven development, source control management, and test automation</li><li>
Proven ability to work across cross-functional teams in a fast-paced, dynamic environment</li><li>
Excellent collaborative problem-solving and communication skills, with the ability to influence without authority</li><li>
A track record of designing and delivering scalable, reliable, and high-quality data platforms</li><li>
Nice to have: experience building data pipelines for Digital Marketing use cases</li></ul>
Work Location
 
San Francisco 
 
The above locations are eligible offices for this role. The locations have been determined to foster in-person collaboration with this role&rsquo;s team or the related business lines. We utilize a hybrid work model, and our teams are in-office Tuesdays, Wednesdays, and Thursdays. In-person attendance is essential for this role&rsquo;s success, and remote placement will not be considered. LendingClub offers relocation, based on actual job level.
 
 
Time Zone Requirements
 
Primarily PT 
 
While the position will primarily work local hours, LendingClub is headquartered in Pacific Time and our ideal candidate will be flexible working across time zones when necessary. 
 
Travel Requirements
 
As needed travel to LendingClub offices and/or other locations, as needed.
 
 
Compensation
 
The target base salary range for this position is 198,000-230,000. The base salary of the role will be determined by job-related knowledge, experience, education, skills, and location. Base salary is just one part of LendingClub&rsquo;s Total Rewards package. You may also be eligible for long-term awards (equity) and an annual bonus (which is based on company performance, employee performance and eligible earnings). 
 
We&rsquo;re creating new financial services solutions for our members based on fairness, simplicity, and heart, and we treat our employees the same way. We offer a competitive benefits package that includes medical, dental and vision plans for employees and their families, 401(k) match, health and wellness programs, flexible time off policies for salaried employees, up to 16 weeks paid parental leave and more.
 
 
#LI-Hybrid
 
#LI-JT1 
 
LendingClub is an equal opportunity employer and dedicated to diversity, equity, and inclusion in the workplace. We do not discriminate on the basis of race, religion, color, national origin, sex (including pregnancy, childbirth, reproductive health decisions, or related medical conditions), gender, gender identity, gender expression, sexual orientation, age, marital status, veteran status, disability status, political views or activity, or other applicable legally protected characteristics. We believe that a variety of perspectives will make our teams and business stronger as we work together to transform the traditional banking system. 
We are committed to providing reasonable accommodations for qualified individuals with disabilities in our job application process. If you need assistance or an accommodation due to a disability, please contact us at interviewaccommodations@lendingclub.com.</div>

Principal Data Engineer

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

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