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learning engineer

Hayden AI

<div>
 
 About Us
 
 
 
 At Hayden AI, we are on a mission to harness the power of computer vision to transform the way transit systems and other government agencies address real-world challenges.
 
 
 From bus lane and bus stop enforcement to transportation optimization technologies and beyond, our innovative mobile perception system empowers our clients to accelerate transit, enhance street safety, and drive toward a sustainable future.
 
 
 
 About The Deep Learning Team
 
 
 
 The Deep learning team&rsquo;s work is at the crux of Hayden AI&rsquo;s solutions to its customers. The team is responsible for building and maintaining advanced models that are able to perceive the world and the knowledge produced by the models is used downstream to implement various customer use-cases. The team runs a number of models both in the cloud as well as on the edge device serving different scenarios. The team also works closely with the platform team to build out Hayden AI&rsquo;s MLOps infrastructure to achieve better scale and reliability.
 
 
 
 About The Role
 
 
 
 As a Deep Learning Engineer at Hayden, you will make key contributions towards building foundational AI capabilities for Hayden that will enable solving complex problems for Hayden AI Customers. In this role, you will build, train, evaluate and deploy state of the art ML models in production at scale. You will report into the Director of AI within the Perception Org and work closely with other engineering as well as product teams to deliver value to Hayden AI customers. We are looking for someone who has familiarity with or growing expertise in at least one of the verticals below &mdash; depth in a specific area is valued but not required on day one.
 
 
 <ul>
 <li>
 3D vision models to predict depth and 3D structure,
 </li>
 <li>
 Video/temporal behavior models to predict intent,
 </li>
 <li>
 Deep understanding of Vision language models and hands-on experience fine tuning them,
 </li>
 <li>
 Deep understanding of foundation models in perception.
 </li>
 <li>
 Deep knowledge of Nvidia edge device stack for running ML models and cuda know-how in terms delivering highly optimized models
 
 
 </li>
 </ul>
 This position is based in San Francisco and follows a hybrid schedule with at least 3 days in-office per week.
 
 
 
 
 Key Responsibilities
 
 
 
 
 Below are your primary responsibilities. These represent the core areas where you&rsquo;ll make an impact. As part of a rapidly evolving team, we look forward to your impact expanding over time.
 
 
 <ul>
 <li>
 Come up with solutions to complex problems in perception using deep learning.
 </li>
 <li>
 Articulate ideas both verbally and in written form culminating in clear design and project plan docs for their work
 </li>
 <li>
 Contribute to team roadmap and planning
 </li>
 <li>
 Work with cross functional teams across the company and contribute towards delivery of end to end solutions for the customer
 
 
 </li>
 </ul>
 
 Required Qualifications
 
 
 
 The qualifications below outline the experience and skills most relevant to success in this role. We recognize that skills and potential come in many forms, and we welcome diverse experiences that advance our mission.
 
 
 <ul>
 <li>
 Experience: 1-2 years experience building and deploying machine learning models for perception in production settings.
 </li>
 <li>
 Core Skills: Solid hands-on experience designing, training, and evaluating machine learning models for perception. Demonstrated ability to independently build ML pipelines and deploy models to cloud environments (AWS, GCP, or Azure); familiarity with MLOps practices including experiment tracking, model versioning, and automated workflows. Hands-on experience in PyTorch, Python, and related skills.
 </li>
 <li>
 Personal Attributes:Good communicator, self starter and ability to collaborate with others, quick learner who can adapt to a fast paced startup culture
 </li>
 <li>
 Education: Bachelors or Masters in Computer Science or related field
 </li>
 <li>
 Nice to Have: Experience working in perception problems in self driving car companies that aligns closely with the work of this team.
 
 
 </li>
 </ul>
 Compensation Range: $161,637 - $175,000
 
 
</div>

Deep Learning Engineer

Quantcast

<div><div>At Quantcast, we don't just build advertising technology, we revolutionize how it works. Our AI-powered Demand Side Platform (DSP) connects the world's most ambitious marketers with their ideal audiences across the open internet, delivering results that actually move the needle. Since 2006, we've been the industry's trailblazer, launching the first AI-powered measurement platform for publishers and the first AI-driven DSP. Our AI doesn't just optimize&mdash;it delivers the measurable outcomes that matter most to our clients, giving them the competitive edge they need in a crowded marketplace. Ready to join the team that's defining the future of digital advertising?</div><div></div><div> 
The Modeling team is responsible for Machine Learning (ML) systems at Quantcast. We build and maintain multiple ML products that price millions of bid requests per second in a real-time auction environment to maximize advertiser outcomes. For each bid our models predict age, gender, viewability, fraud, advertiser relevance and many more characteristics. Using NLP, clustering and LLMs we build topics in multiple languages to help our advertisers target customers interested in relevant content.</div>
<div></div>
<div>As a Machine Learning Engineer you care about the health and maintainability of our systems and the velocity of the engineering teams. You explore data, research new algorithms, experiment with proof of concepts, and build out scalable real-time production systems to tackle challenges the company faces.</div>
<div></div>
<h3 class="jobSectionHeader">What you'll do:</h3><ul>
<li>Design, code, test, and debug ML applications and constantly improve large-scale global systems that respond to millions of real-time requests per second efficiently.</li>
<li>Run Machine Learning experiments to test new modeling ideas.</li>
<li>Write clean, efficient, and maintainable code using industry best practices.</li>
<li>Collaborate closely with engineering teams, to deliver high-quality ML products. Participate in code reviews and provide constructive feedback to team members.</li>
<li>Identify performance bottlenecks and optimize system components for enhanced scalability.</li>
<li>Keep up to date with developments in machine learning outside the company.</li>
</ul><h3 class="jobSectionHeader">Who you are:</h3><ul>
<li>New Grad to 1 year of experience.</li>
<li>You must be work-authorized in the United States without the need for employer sponsorship.</li>
<li>This is a hybrid role based in our San Francisco office. To ensure a manageable commute for in-office days, candidates must reside within a 60-mile radius of San Francisco, CA. No relocation candidates at this time.</li>
<li>Fluency in Python, Java or similar programming languages.</li>
<li>Strong skills in mathematics and statistics.</li>
<li>Working knowledge of ML algorithms such as classification, control systems, optimization, clustering, LLMs or recommendation systems.</li>
<li>An interest in distributed system and software design, concurrent algorithms, data structures, and software engineering.</li>
</ul><div>The expected base salary range for this role is $151,300 to $178,000.</div><div></div><div> 
At Quantcast, we craft offers that reflect your unique skills, expertise, and geographic location. On top of a competitive salary, this position includes a performance bonus, equity, and a comprehensive benefits package. For more details, visit our Careers Page and see how we support our team. We are headquartered in San Francisco with offices around the world. Quantcast is an Equal Opportunity Employer. Please see the Applicant Privacy Notice for details on our applicant privacy policy. Join the team that unlocks potential.</div></div><div></div>

Machine Learning Engineer

Salesforce

<div>To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts.
Job Category
Software Engineering
Job Details
About Salesforce
Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn&rsquo;t a buzzword &mdash; it&rsquo;s a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all.
Ready to level-up your career at the company leading workforce transformation in the agentic era? You&rsquo;re in the right place! Agentforce is the future of AI, and you are the future of Salesforce.
Lead member of technical staff - Machine Learning Engineering
Job Category: Software Engineering
Job Details
About Salesforce
We&rsquo;re Salesforce, the Customer Company, inspiring the future of business with AI+ Data +CRM. Leading with our core values, we help companies across every industry blaze new trails and connect with customers in a whole new way. And, we empower you to be a Trailblazer, too &mdash; driving your performance and career growth, charting new paths, and improving the state of the world. If you believe in business as the greatest platform for change and in companies doing well and doing good &ndash; you&rsquo;ve come to the right place.
We are a foundation machine learning platform team within the Trust Intelligence Platform organization with a main focus to build and accelerate scalable and resilient machine learning pipelines across the security engineering organization.
We are looking for a highly motivated, hands-on lead machine learning engineer with a strong business understanding of cybersecurity problems, who acts as a force multiplier security data scientist for our security organization. The lead will not simply build models; they will architect the data-driven strategy for our threat detection capabilities.
Your impact:
<ul><li>Shape the Defense Strategy: You will own the decision-making process&mdash;translating vague security threats into concrete mathematical problems. By championing a rapid prototyping culture, you will validate hypotheses in days rather than months, ensuring our engineering resources are focused only on high-value detections while killing low-signal ideas early.
</li><li>Detect the &quot;Unknown Unknowns&quot;: You will lead the evolution of our threat detection, introducing more advanced probabilistic modeling, graph analytics, supervised and unsupervised learing. Your work will expose sophisticated threats&mdash;such as active system intrusions, lateral movement, beaconing, and insider attacks&mdash;that evade traditional defenses, directly reducing the organization's risk surface.
</li><li>Elevate the Organization: You will act as a force multiplier, mentoring junior scientists and engineers, and building the internal tooling, feature stores, and libraries that make the whole team faster. You will influence the broader security engineering roadmap to ensure a closed loop security telemetry that is treated as a first-class citizen.
</li><li>Operationalize Intelligence: By prioritizing engineering rigor (CI/CD, scalable code) and adversarial resilience, you will deliver production-grade models that the SOC actually trusts&mdash;minimizing &quot;alert fatigue&quot; and maximizing analyst efficiency.
</li></ul>Required skills:
<ul><li>Extensive experience (3-5+ years) in data science, with at least 2+ years dedicated to the cybersecurity domain designing, implementing and deploying systems of anomaly detection, clustering, and graph models in production.
</li><li>Hands-on comfort with high-volume logs and proficiency with Spark/Pyspark, Snowflake, Flink and streaming services such as Apache Kafka
</li><li>Deep understanding and application of containerization (Docker) and workflow orchestration (Kubernetes, Apache Airflow) for automated ML pipelines.
</li><li>Mastery of Python programming, including proficiency in leading ML frameworks (TensorFlow, PyTorch) and adherence to software engineering best practices.
</li><li>Demonstrated success in implementing comprehensive MLOps methodologies, encompassing CI/CD pipelines, testing protocols, and model performance monitoring.
</li><li>Solid foundation in feature engineering techniques and the implementation of feature stores.
</li><li>Experience in formulating ML governance policies and ensuring adherence to data security regulations.
</li><li>Ability to explain complex statistical concepts to non-technical stakeholders and executive leadership.
</li><li>Proven ability to manage scope, timelines, and stakeholder expectations across multiple organizations.
</li><li>High degree of autonomy with the ability to look at a vague business problem and structure a data-driven solution without needing a predefined roadmap.
</li></ul>Preferred skills:
<ul><li>Masters or PhD in a quantitative field
</li><li>Expertise in advanced Natural Language Processing (NLP) methodologies.
</li><li>Experience contributing to open-source security data science tools.
</li><li>Presentations at major security conferences (Black Hat, DEF CON, BSides) or data conferences.
</li><li>Background in offensive security (Penetration Testing/Red Teaming) with an &quot;attacker's mindset.&quot;
</li><li>Demonstrated experience conducting research or working collaboratively with Machine Learning (ML) research teams.
</li><li>Previous experience in a mentoring role for junior engineers.
</li><li>Track record of publications and/or patents in quantitative disciplines.
</li></ul>Unleash Your Potential
When you join Salesforce, you&rsquo;ll be limitless in all areas of your life. Our benefits and resources support you to find balance and be your best , and our AI agents accelerate your impact so you can do your best . Together, we&rsquo;ll bring the power of Agentforce to organizations of all sizes and deliver amazing experiences that customers love. Apply today to not only shape the future &mdash; but to redefine what&rsquo;s possible &mdash; for yourself, for AI, and the world.
Accommodations
If you need a reasonable accommodation during the application or the recruiting process, please submit a request via this Accommodations Request Form .
Please note that Salesforce uses artificial intelligence (AI) tools to help our recruiters assess and evaluate candidates&rsquo; resumes and qualifications throughout the recruiting process. Humans will always make any candidate selection and hiring decisions. Please see our Candidate Privacy Statement for more information about how we use your personal data and your rights, including with regard to use of AI tools and opt out options.
Posting Statement
Salesforce is an equal opportunity employer and maintains a policy of non-discrimination with all employees and applicants for employment. What does that mean exactly? It means that at Salesforce, we believe in equality for all. And we believe we can lead the path to equality in part by creating a workplace that&rsquo;s inclusive, and free from discrimination. Know your rights: workplace discrimination is illegal. Any employee or potential employee will be assessed on the basis of merit, competence and qualifications &ndash; without regard to race, religion, color, national origin, sex, sexual orientation, gender expression or identity, transgender status, age, disability, veteran or marital status, political viewpoint, or other classifications protected by law. This policy applies to current and prospective employees, no matter where they are in their Salesforce employment journey. It also applies to recruiting, hiring, job assignment, compensation, promotion, benefits, training, assessment of job performance, discipline, termination, and everything in between. Recruiting, hiring, and promotion decisions at Salesforce are fair and based on merit. The same goes for compensation, benefits, promotions, transfers, reduction in workforce, recall, training, and education.
In the United States, compensation offered will be determined by factors such as location, job level, job-related knowledge, skills, and experience. Certain roles may be eligible for incentive compensation, equity, and benefits. Salesforce offers a variety of benefits to help you live well including: time off programs, medical, dental, vision, mental health support, paid parental leave, life and disability insurance, 401(k), and an employee stock purchasing program. More details about company benefits can be found at the following link: https://www.salesforcebenefits.com.Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Salesforce will consider for employment qualified applicants with arrest and conviction records.
At Salesforce, we believe in equitable compensation practices that reflect the dynamic nature of labor markets across various regions.&amp;#xa;&amp;#xa;The typical base salary range for this position is $172,500 - $260,100 annually. In select cities within the San Francisco and New York City metropolitan area, the base salary range for this role is $207,800 - $285,800 annually.&amp;#xa;&amp;#xa;The range represents base salary only, and does not include company bonus, incentive for sales roles, equity or benefits, as applicable.</div>

Lead Machine Learning Engineer - LMTS

Apple

The Apple Services Engineering AI/ML organization is hiring a Machine Learning Engineer to join the Search team for Video catalog. 
 
Our team builds the core intelligence that powers search discovery experiences in the Apple TV App, Siri, and Spotlight cross platforms, helping users effortlessly find and enjoy the content they love. We are a collaborative, high-impact team that values innovation, craftsmanship, and end-to-end ownership from idea to launch. Our systems combine large-scale data, modern retrieval and ranking models, and a deep commitment to user privacy. 
 
Join us, you&rsquo;ll develop scalable systems and machine learning models that drive search relevance, personalization, and understanding of content at scale. Working closely with cross-functional partners in product and design, you&rsquo;ll translate cutting-edge research in advanced machine learning and generative AI into secure and delightful production features used by millions every day. 
 
Description 
 
As a Machine Learning Engineer on the Video Search team, within the Apple Services Engineering AI/ML organization, you will design and deploy large-scale ML systems that power search and discovery across Apple platforms. 
 
You&rsquo;ll apply machine learning, natural language understanding, and generative AI to model user intent and deliver relevant, personalized results. Your work will involve building and optimizing cutting edge data processing, ML models, retrieval pipelines, and ranking systems that operate at global scale and under strict privacy standards. 
 
This is a hands-on role where you will collaborate closely with cross-functional teams to bring advanced ML technologies into production-shaping how users discovery content they love in Apple TV app, cross Apple TV partners on Apple Platforms, also through Siri and Spotlight.&quot;,&quot;responsibilities&quot;:&quot;Solve complex research problems and implement solutions from concept to execution. 
 
Design and implement retrieval and ranking systems using semantics and user context. 
 
Build and deploy ML, NLP and LLM models to improve search relevance and personalization. 
 
Analyze data and model performance to identify opportunities for search quality enhancement. 
 
Develop automated tests for continuous integration and ensure successful production deployment. 
 
Conduct A/B tests to measure search improvements. 
 
Collaborate with cross-functional teams to innovate intelligent video catalog text search. 
 
Enhance search recall and ranking for global Apple devices across all platforms and languages. 
 
Utilize big data tech to evaluate content discovery features. 
 
Ensure systems meet Apple&rsquo;s privacy, efficiency, and user experience standards. 
 
Preferred Qualifications 
 
Experience with search or recommendation systems, and semantic retrieval or vector databases. 
 
Expertise in transformer architectures, embeddings, and retrieval or ranking models. 
 
Experience in applying or fine-tuning LLMs for understanding and generation tasks. Familiarity with prompt design, context management, RAG and Agentic architectures and solutions. 
 
Exposure to evaluation and safety frameworks for LLM-based systems. 
 
Knowledge of reinforcement learning and other modern post training practices for LLMs. 
 
Passion for developing intelligent, human-centered experiences to enhance content discovery. 
 
Minimum Qualifications 
 
Experience in machine learning, NLP, IR, or more recently Large Language Model ( LLMs). 
 
Strong programming skills in Python, Java and Go for building scalable ML systems. 
 
Hands-on expertise in ML libraries such as PyTorch, JAX, TensorFlow for model training and deployment. 
 
Ability to translate product goals into technical solutions, improving user experience. 
 
Strong communication, collaboration, and analytical problem-solving skills. 
 
In-depth knowledge of search and information retrieval fundamentals, including indexing and ranking. Experience with retrieval and ranking algorithms and building big data pipelines using Hadoop, Java, Scala, Spark and more. 
 
Industrial experience in search, classification, recommendation systems, or related fields. 
 
Familiarity with A/B testing and data-driven product development. 
 
Passionate about creating products loved by customers at Apple. 
 
Bachelor&rsquo;s degree or higher (or equivalent practical experience) in Computer Science, Machine Learning, Natural Language Processing, Artificial Intelligence, or a related field. 
 
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.

Machine Learning Engineer, ASE Search Team

Plaid

<div><div><h2 class="jobSectionHeader">Location</h2>
San Francisco HQ; New York City Office; Seattle Office
</div><div><h2 class="jobSectionHeader">Address</h2>
1098 Harrison Street, San Francisco, California, 94103
</div><div><h2 class="jobSectionHeader">Employment Type</h2>
Full time
</div><div><h2 class="jobSectionHeader">Location Type</h2>
Hybrid
</div><div><h2 class="jobSectionHeader">Department</h2>
All DepartmentsData
</div><div><h2 class="jobSectionHeader">Compensation</h2>
<ul><li>$211,680 &ndash; $272,160</li>
</ul><div>Additional compensation in the form(s) of equity and/or commission are dependent on the position offered. Plaid provides a comprehensive benefit plan, including medical, dental, vision, and 401(k). Pay is based on factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience and skillset, and location. Pay and benefits are subject to change at any time, consistent with the terms of any applicable compensation or benefit plans.</div></div></div>
 <div><div>We believe that the way people interact with their finances will drastically improve in the next few years. We&rsquo;re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid&rsquo;s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Washington D.C., London and Amsterdam.
The Data Foundation &amp; AI team sits within Plaid&rsquo;s Data organization and is responsible for building the shared machine learning and AI infrastructure that powers innovation across Plaid&rsquo;s product portfolio. The team transforms Plaid&rsquo;s unique financial network data into scalable, general-purpose representations that can be leveraged by teams throughout the company. Working across the full model lifecycle, the team develops and curates pretraining datasets, trains and evaluates foundation models, and operates production-grade serving and monitoring systems to ensure reliable, high-impact AI capabilities at scale.
As a Research Scientist, you will advance Plaid&rsquo;s foundation models by developing novel model architectures, pretraining objectives, and fine-tuning strategies that generalize across a broad range of financial and product applications. You will contribute to the design, development, and operation of production machine learning systems, working across the full stack from data and feature engineering to training pipelines, model serving, and monitoring. The role also involves creating comprehensive evaluation frameworks that assess model performance across diverse tasks and use cases, ensuring robust and reliable outcomes. In close partnership with product and engineering teams, you will adapt foundation models to solve specific business challenges, validate their impact through rigorous experimentation, and translate research advances into production capabilities. You will also share insights and results with both internal and external audiences, helping drive innovation and elevate the practice of machine learning and AI across Plaid.
Responsibilities:
<ul><li>Building a foundation model on one of the world&rsquo;s richest financial datasets that no one else has.
</li><li>Doing research that ships: moving from experimentation and prototypes to production systems serving real customers.
</li><li>Working across the full ML stack, from pretraining objectives and architectures to serving infrastructure and monitoring.
</li><li>Collaborating with a high-caliber team and seeing your work amplify the capabilities of multiple product teams.
</li><li>Helping hundreds of millions of consumers achieve greater financial freedom through data-driven products.
</li></ul>Qualifications:
<ul><li>MS or PhD in ML/AI/CS/Stats/Applied Math (or closely related). PhD preferred but not required &mdash; candidates with equivalent industry research and production experience will be considered.
</li></ul><ul><li>1-3 years of industry experience building and deploying ML models, with evidence of both research depth and production delivery
</li><li>Strong applied ML research skills with production delivery experience
</li><li>Depth in Transformers/LLMs, representation learning, or large-scale model training
</li><li>Distributed training experience and strong Python + software engineering fundamentals
</li><li>Fintech / financial data domain experience - Nice to have
</li><li>Demonstrated ability to ship models to production (not just prototype) - Nice to have
</li><li>External publications or open-source contributions - Nice to have
</li></ul>Our mission at Plaid is to unlock financial freedom for everyone. To support that mission, we seek to build a diverse team of driven individuals who care deeply about making the financial ecosystem more equitable. We recognize that strong qualifications can come from both prior work experiences and lived experiences. We encourage you to apply to a role even if your experience doesn't fully match the job description. We are always looking for team members that will bring something unique to Plaid!
Plaid is proud to be an equal opportunity employer and values diversity at our company. We do not discriminate based on race, color, national origin, ethnicity, religion or religious belief, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, military or veteran status, disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state, and local laws. Plaid is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance with your application or interviews due to a disability, please let us know at accommodations@plaid.com. 

Additional compensation in the form(s) of equity and/or commission are dependent on the position offered. Plaid provides a comprehensive benefit plan, including medical, dental, vision, and 401(k). Pay is based on factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience and skillset, and location. Pay and benefits are subject to change at any time, consistent with the terms of any applicable compensation or benefit plans.
Compensation Range: $211,680 - $272,160</div></div>

Machine Learning Engineer (Research Scientist) - DFAI

Hellyeah AI

AI Engineer - Learn Engine: Intelligence &amp; OptimizationLocation: San Francisco HQ; ShanghaiAddress: San Francisco, CaliforniaEmployment Type: Full timeLocation Type: HybridDepartment: EngineeringCompensation: San Francisco $200K - $1M + 0.5% - 3% equityBuild the brain of an autonomous growth OS. The system you create will manage millions in ad spend and get measurably smarter with every dollar. This is the moat: every competitor has humans optimizing campaigns manually. You are building the intelligence layer that compounds. The Platform engineer creates the tools; you create the decisions. Together you build something nobody else has.Must Have- Has built recommendation, optimization, or decision systems where outputs improve future inputs.- Strong statistical reasoning and experimentation judgment under noisy real-world data.- Strong LLM orchestration or agent-system experience for reasoning over campaign context.- Can design optimization policies, scoring systems, or automated recommendation loops.- AI-first development workflow and ability to ship production systems quickly.Nice to Have- Ad-tech optimization patterns: bid management, budget allocation, ROAS optimization.- Reinforcement learning experience.- Hyperparameter optimization experience.- Model fine-tuning experience.- Experience building agent-driven automation where LLM agents take actions.- Background in growth engineering, performance marketing, or data science.- Experience with Mastra or similar agent orchestration framework.What You Will OwnOwn the intelligence and optimization layer of Learn Engine. Build recommendation engines for bid changes, budget reallocation, pause/boost decisions, and postback optimization. Turn SSOT campaign data into high-quality optimization guidance and closed-loop decision systems. Define how the system learns from outcomes and continuously improves campaign strategy over time. This role owns decision quality, optimization policy, and learning loops, not platform plumbing or simulator infrastructure.Pay: $200,000.00 - $1,000,000.00 per yearWork Location: In person

AI Engineer - Learn Engine: Intelligence & Optimization

MetaVoice

<div>
 
 At MetaVoice, our goal is an
 
 AI for voice conversations that's as natural as talking to a person.
 
 
 
 
 
 
 Today's voice AI fails at real-world conversations. It&rsquo;s slow, turn-based like a walkie-talkie, breaks with interruptions, and doesn&rsquo;t understand emotion.
 
 
 
 
 
 Developers can't build compelling experiences and users disengage. This limits voice AI to simple receptionist tasks and basic customer support, blocking meaningful services (sales, therapy, coaching) where dialogue and emotional intelligence matter most. Scaling current tech does not work.
 
 
 
 
 
 We're building voice AI that's as natural as talking to a person. Our approach is a duplex speech-to-speech model that learns conversational behaviour directly from data.
 
 
 
 
 
 MetaVoice is founded by:
 
 <ul>
 <li>
 Sid, founding engineer at Wayve.ai ( $2.5B+ raised)
 </li>
 <li>
 Vatsal, co-created Alexa&rsquo;s first AI voice.
 </li>
 </ul>
 
 
 
 
 We&rsquo;re passionate about building products people truly love.
 
 
 
 
 
 
 The role
 
 
 
 This is a founding engineer role owning the ML data &amp; evaluation platform that model development runs on.
 
 
 
 
 
 
 Who we're looking for
 
 
 
 You&rsquo;ve worked on distributed ML infra and understand that model quality is often a data problem. You:
 
 <ul>
 <li>
 Have a track record of taking initiative and learning things quickly.
 </li>
 <li>
 Care deeply about the people using the systems you build, whether they're researchers, operators or customers
 </li>
 <li>
 Have built something yourself (startup, side project, etc) or worked in an early-stage startup
 </li>
 </ul>
 
 
 
 
 
 What you've done
 
 
 <ul>
 <li>
 Built distributed ML data pipelines to generate datasets for training and evaluation, including curation, sampling and slicing.
 </li>
 <li>
 Built and operated MLOps infrastructure to train, evaluate and serve ML models, including versioning, orchestration and observability.
 </li>
 <li>
 Built eval systems to measure model quality and catch regressions.
 </li>
 <li>
 Developed internal tools and workflows for researchers and operators to explore data and iterate fast.
 </li>
 </ul>
 
 Strong plus:
 
 <ul>
 <li>
 Designed data flywheels: automated data collection and model update loops.
 </li>
 <li>
 Designed evaluation frameworks: automated or human-in-the-loop
 </li>
 <li>
 Experience with speech, audio, or conversational datasets
 </li>
 </ul>
 
 
 
 
 
 What we offer
 
 
 <ul>
 <li>
 Change the world (when we succeed)
 </li>
 <li>
 Environment to do the best work of your life.
 </li>
 <li>
 Small, humble and talent dense team.
 </li>
 <li>
 Opportunity to make cutting-edge research work in real-world scenarios.
 </li>
 <li>
 Work with 100+ TBs of data and 10B+ parameter models.
 </li>
 </ul>
 
 
 
 
 
 Culture
 
 
 <ul>
 <li>
 We're an in-person team in San Francisco &amp; love working together. It helps us learn from one another and make decisions quickly.
 </li>
 <li>
 We ship fast &amp; obsess over making customers happy.
 </li>
 <li>
 We offer high autonomy, allowing everyone to do their best work.
 </li>
 </ul>
 
 
 
 
 
 Compensation &amp; Benefits
 
 
 <ul>
 <li>
 Significant equity as a founding team member.
 </li>
 <li>
 $180K-300K base.
 </li>
 <li>
 Immigration support, we are immigrants ourselves.
 </li>
 <li>
 Fully covered medical, dental, and vision insurance
 </li>
 </ul>
 
 
 
 
 
 Interview Process
 
 
 
 We move with speed, and aim to go from first interview to offer within a week.
 
 <ul>
 <li>
 
 Vibe check, 30 mins:
 
 chat with one of the founders about the mission, product and your background
 </li>
 <li>
 
 Technical Interview: 30 mins
 
 </li>
 <li>
 
 Culture interview: 30 mins
 
 </li>
 <li>
 
 Onsite co-work:
 
 Spend half a day with us. We'll build and iterate on a problem together.
 </li>
 </ul>
 
 
</div>

ML Platform Engineer (Data & Evals)

Explore 93+ Learning engineer in San Francisco this July. Find roles across top companies and apply now to boost your career.