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Shields Group Search

<div>
 
 
 Machine Learning Engineer: LLM Interpretability &amp; Systems
 
 
 
 
 Location:
 
 San Francisco, CA - 5 days a week onsite
 
 
 
 
 
 
 About Our Client &amp; The Mission
 
 
 
 We are partnering with a San Francisco-based AI company pushing open-source Large Language Models beyond what most teams think is possible. They do not just prompt models. They open the black box. By leveraging cutting-edge interpretability techniques (XAI), they manipulate model internals to push open-source models to rival and beat Frontier models in both capability and cost-efficiency.
 
 
 
 
 
 
 They are primarily looking for a highly driven Machine Learning Engineer who can take complex, cutting-edge XAI research and translate it into robust, deployable production systems.
 
 
 
 
 
 
 
 The Role
 
 
 
 This is not a prompt engineering or RAG-wrapper position. You will be operating deep within the stack, interacting directly with model weights, activations, and architectures. Your mandate is simple but exceptionally difficult: state exactly how a model can be improved for a specific purpose using interpretability, and then build the system to do it.
 
 
 
 
 
 
 What You Will Do:
 
 
 <ul>
 <li>
 
 Translate Research to Production:
 
 Take and improve the latest papers in mechanistic interpretability, activation engineering, and representation engineering and turn them into scalable production code.
 </li>
 <li>
 
 Model Surgery:
 
 Apply techniques like control vectors, activation patching, and feature extraction to fundamentally alter and enhance model capabilities.
 </li>
 <li>
 
 Maximize Open-Source:
 
 Squeeze every ounce of performance out of open-weight models to make them hyper-efficient and highly competitive against closed-source Frontier models.
 </li>
 <li>
 
 Build the Pipeline:
 
 Design and implement the infrastructure required to deploy these modified, highly-optimized models into production environments.
 </li>
 </ul>
 
 
 
 
 
 Who You Are
 
 
 <ul>
 <li>
 
 Deep Technical Fundamentals:
 
 You possess a rigorous understanding of Transformer architectures, PyTorch internals, and the mathematical foundations of deep learning.
 </li>
 <li>
 
 Beyond the API:
 
 You have hands-on experience training, fine-tuning, and dissecting LLMs. You understand the difference between superficial augmentation and structural model improvement.
 </li>
 <li>
 
 High Agency &amp; Conviction:
 
 You do not wait for tasks to be assigned. You identify bottlenecks, propose deeply technical solutions, and execute them with absolute conviction.
 </li>
 <li>
 
 Mission-Obsessed:
 
 You are fundamentally aligned with the mission to democratize state-of-the-art AI capabilities through open-source optimization.
 </li>
 </ul>
 
 
 
 
 
 The Anti-Persona (Who this role is NOT for)
 
 
 
 If your primary experience involves calling the OpenAI API, chaining together prompts with LangChain, or building standard RAG pipelines without touching the underlying model infrastructure, this role is not a fit. We need someone who can go into the matrix, not just interact with the interface.
 
 
 
 
 
 
 What We Offer
 
 
 <ul>
 <li>
 
 Aggressive Equity &amp; Compensation:
 
 Highly competitive salaries and significant equity stakes to incentivize the hard work required. $180-260K, 0.5-1% equity
 </li>
 <li>
 
 Impact:
 
 Pioneering work at the intersection of XAI and production engineering, directly shaping the capabilities of the core product.
 </li>
 <li>
 
 Autonomy:
 
 A high-trust environment where technical conviction is valued and supported.
 </li>
 </ul>
</div>

Machine Learning Engineer

Stealth Startup

<div>
 
 The Culture We Are Building
 
 <ul>
 <li>
 We are assembling a lean group of generalists. While we welcome experience, we're primarily looking for creative, curious problem solvers with a keen interest in open-ended, technically challenging problems.
 </li>
 <li>
 We are looking to hire in-person in San Francisco. We believe that having a community is critical for learning, growth, and camaraderie. We will pay generously for relocation assistance.
 </li>
 <li>
 We want to invest heavily in our team's growth. We will happily sponsor conference attendance and encourage projects and research. If there's something that will help you grow as an engineer, researcher, or designer, we actively want to support it.
 </li>
 <li>
 We believe in compensating exceptional talent exceptionally well. We are an early stage company, but are committed to working with candidates we're excited about to ensure they are excited too.
 </li>
 <li>
 We have an academic as well as competitive spirit. We enjoy thought exercises, math puzzles, intellectual inquiry, and vigorous debates.
 </li>
 </ul>
 
 
 
 
 Role: Machine Learning Engineer
 
 
 Design, train, and ship models that guide reviewers to the most important changes, resolve merge conflicts more safely, and make diffs understandable through semantic grouping and summarization. You'll combine retrieval, code‑aware modeling, and small‑model techniques to deliver low‑latency, cost‑effective ML in production.
 
 
 Responsibilities
 
 <ul>
 <li>
 -Train and fine‑tune models that predict the next location a reviewer should focus on: ranking files, hunks, and comments
 </li>
 <li>
 -Build ML‑assisted merge conflict resolution: align and reconcile hunks using code context/AST, propose safe resolutions, and explain changes
 </li>
 <li>
 -Develop semantic change grouping and diff visualization: detect refactors/moves, cluster related edits across files, and generate concise summaries
 </li>
 <li>
 -Evaluate retrieval and RAG for pull requests: retrieve relevant context, discussions, and history to improve grouping, prioritization, and summarization
 </li>
 <li>
 -Design offline/online evaluation: labeled datasets, ranking metrics (e.g., MAP/NDCG), A/B experiments, and human‑in‑the‑loop feedback
 </li>
 <li>
 -Operate efficient inference: apply small‑model techniques (LoRA, supervised fine‑tuning, distillation, quantization)
 </li>
 </ul>
 
 Qualifications
 
 <ul>
 <li>
 -Strong background in deep learning research and implementation, with experience in modern architectures and concepts (transformers, MoE, reasoning, etc.)
 </li>
 <li>
 -Track record publishing results at top-tier conferences (NeurIPS, ICML, ICLR)
 </li>
 <li>
 -Expertise in Python and ML frameworks (PyTorch, JAX)
 </li>
 <li>
 -Experience with MLOps and model optimization (ONNX, TensorRT, serving infrastructure)
 </li>
 <li>
 -Familiarity with cost‑effective techniques: LoRA, supervised fine‑tuning, distillation, and quantization for small/efficient models
 </li>
 <li>
 -Familiarity with systems programming across the stack: Rust/C/C++, CUDA/GPU optimization, and kernel-level performance tuning
 </li>
 <li>
 -Experience with reinforcement learning, particularly in systems optimization and decision-making tasks
 </li>
 </ul>
 
 
</div>

Machine Learning Engineer [32985]

Handshake

<div><h1 class="jobSectionHeader">About Handshake</h1>
Handshake is the career network for the AI economy. 20 million knowledge workers, 1,600 educational institutions, 1 million employers (including 100% of the Fortune 50), and every foundational AI lab trust Handshake to power career discovery, hiring, and upskilling, from freelance AI training gigs to first internships to full-time careers and beyond. This unique value is leading to unparalleled growth; in 2025, we tripled our ARR at scale.
Why join Handshake now:<ul><li>
Shape how every career evolves in the AI economy, at global scale, with impact your friends, family and peers can see and feel</li><li>
Work hand-in-hand with world-class AI labs, Fortune 500 partners and the world&rsquo;s top educational institutions</li><li>
Join a team with leadership from Scale AI, Meta, xAI, Notion, Coinbase, and Palantir, among others</li><li>
Build a massive, fast-growing business with billions in revenue</li></ul><h1 class="jobSectionHeader">
About the Role
</h1>Handshake is hiring an Associate Machine Learning Engineer for the Growth Relevance team. AI is transforming how students navigate their careers, and we're committed to providing innovative, responsible AI-powered solutions that guide students from educational aspirations to meaningful career opportunities. In this role, you will contribute directly to this mission by developing, deploying, and enhancing machine learning systems focused on lifecycle optimization, personalized notifications, and monetization strategies.
You'll join a high-impact team leveraging cutting-edge ML infrastructure, including embedding-based retrieval, Graph Neural Networks, and multi-stage rankers built upon a robust data platform with billions of data points. Your work will drive critical marketplace metrics, enhance user engagement, and contribute to responsible AI practices around explainability, fairness, and quality.
What You'll Do<ul><li>
Innovator: Develop and iterate on machine learning models and features that directly influence user experience across lifecycle, notifications, and monetization &mdash; with guidance from senior engineers.</li><li>
Collaborator: Partner with senior engineers, data scientists, and product managers to develop and iterate on machine learning models that improve product features and user experience.</li><li>
Learner: Grow your technical depth by working alongside experienced ML practitioners, picking up best practices in model development, experimentation, and production deployment.</li></ul><h1 class="jobSectionHeader">
Desired Capabilities
</h1><ul><li>Bachelor&rsquo;s degree in Computer Science, Data Science, or a related field</li><li>
0&ndash;2 years of experience in machine learning, data science, or a related area</li><li>
Proficient in Python, with hands-on experience in frameworks such as scikit-learn, PyTorch, or TensorFlow</li><li>
Strong foundation in core ML concepts, including classification, regression, ranking, and model evaluation</li></ul>
Extra Credit<ul><li>
Master&rsquo;s degree or currently pursuing an advanced degree in a relevant field</li><li>
Exposure to areas such as recommendations, personalization, NLP, deep learning, LLMs, or explainable AI</li><li>
Familiarity with the ML lifecycle (e.g., experiment tracking, model monitoring, feature pipelines)</li><li>
Experience with cloud platforms (GCP, AWS, or Azure)</li><li>
Clear communicator, able to translate technical work for diverse audiences</li><li>
Collaborative mindset with experience working cross-functionally with product, analytics, and engineering teams</li></ul><h1 class="jobSectionHeader">
Perks</h1>
Handshake delivers benefits that help you feel supported&mdash;and thrive at work and in life.
The below benefits are for full-time US employees.
Ownership: Equity in a fast-growing company
Financial Wellness: 401(k) match, competitive compensation, financial coaching
Family Support: Paid parental leave, fertility benefits, parental coaching
Wellbeing: Medical, dental, and vision, mental health support, $500 wellness stipend
Growth: $2,000 learning stipend, ongoing development
Remote &amp; Office: Internet, commuting, and free lunch/gym in our SF office
Time Off: Flexible PTO, 15 holidays + 2 flex days
Connection: Team outings &amp; referral bonuses
Explore our mission, values, and comprehensive US benefits at joinhandshake.com/careers.
Compensation Range: $115K - $144K</div>

Associate Machine Learning Engineer

Uber

<div>About the Role
 
 
The Marketplace Signals team at Uber is responsible for building and optimizing foundational marketplace signals that power user experiences and drive marketplace efficiency. Our team ensures that key signals-such as eyeball ETA, spinner time, and supply reliability indicators-are leveraged effectively across various Uber products and levers, enabling data-driven decision-making and seamless coordination across different business functions.
 
 
What You'll Do
<ul><li>Develop and optimize ML models to enhance key marketplace signals (e.g., ETA predictions, supply availability metrics, demand forecasts).
</li><li>Collaborate with cross-functional teams (Pricing, Matching, Driver Incentives, etc.) to ensure marketplace signals are effectively utilized.
</li><li>Improve operational efficiency by building a centralized, scalable system for marketplace signals that serves multiple use cases.
</li><li>Leverage cutting-edge ML techniques (deep learning, probabilistic modeling, reinforcement learning, etc.) to continuously refine marketplace signals.
</li></ul>Basic Qualifications
<ul><li>B.S. in Statistics, Mathematics, Computer Science, or Machine Learning
</li><li>2 years of experience in software engineering with an emphasis on data-driven methodologies, deep learning, and online experimentation
</li><li>Strong problem-solving skills, with expertise in ML methodologies
</li><li>Experience in applying ML, statistics, or optimization techniques to solve large-scale real-world problems (e.g. ads tech, recommender systems)
</li><li>Experience in ML frameworks (e.g. Tensorflow, Pytorch, or JAX) and complex data pipelines; programming languages such as Python, Spark SQL, Presto, Go, Java
</li></ul>Preferred Qualifications
<ul><li>3+ years of experience in software engineering specializing in applied ML methods
</li><li>Experience in designing and crafting scalable, reliable, maintainable and reusable ML solutions using deep-learning techniques and statistical methods.
</li><li>Detail-oriented, ownership and truth-seeking mindset.
</li><li>Values and produces analytic evidence and insight, as well as applying them to improve technical solutions.
</li><li>Experience working in a cross-functional and/or cross-business projects, partnering with Product, Scientists, and cross-org leads to shape the team's strategies
</li>Master's degree in Computer Science, Engineering, Mathematics or related field 
<li>
</li></ul> 
 
For San Francisco, CA-based roles: The base salary range for this role is USD$171,000 per year - USD$190,000 per year. You will be eligible to participate in Uber's bonus program, and may be offered an equity award &amp; other types of comp. All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits. More details can be found at the following link https://jobs.uber.com/en/benefits.</div>

Machine Learning Engineer II

<div>The Marketplace Signals team at Uber is responsible for building and optimizing foundational marketplace signals that power user experiences and drive marketplace efficiency. Our team ensures that key signals-such as eyeball ETA, spinner time, and supply reliability indicators-are leveraged effectively across various Uber products and levers, enabling data-driven decision-making and seamless coordination across different business functions.
 
 
What You'll Do
<ul><li>Develop and optimize ML models to enhance key marketplace signals (e.g., ETA predictions, supply availability metrics, demand forecasts).
</li><li>Collaborate with cross-functional teams (Pricing, Matching, Driver Incentives, etc.) to ensure marketplace signals are effectively utilized.
</li><li>Improve operational efficiency by building a centralized, scalable system for marketplace signals that serves multiple use cases.
</li><li>Leverage cutting-edge ML techniques (deep learning, probabilistic modeling, reinforcement learning, etc.) to continuously refine marketplace signals.
</li></ul>What You'll Need
<ul><li>Strong problem-solving skills, with expertise in ML methodologies
</li><li>Experience in applying ML, statistics, or optimization techniques to solve large-scale real-world problems (e.g. ads tech, recommender systems)
</li><li>Experience in ML frameworks (e.g. Tensorflow, Pytorch, or JAX) and complex data pipelines; programming languages such as Python, Spark SQL, Presto, Go, Java
</li></ul>Why Join Us?
<ul><li>Work on high-impact machine learning problems that directly improve Uber's marketplace efficiency.
</li><li>Influence key business levers that optimize Uber's pricing, matching, and rider/driver experience.
</li><li>Build centralized marketplace signals that reduce redundancy and improve operational efficiency.
</li><li>Join a high-caliber, innovative team tackling some of the hardest ML challenges in the industry.</li></ul>
If you're passionate about using ML to optimize real-world systems at a massive scale, we'd love to hear from you!
 
 
What You Will Do:<ul><li>
Develop and optimize ML models to enhance key marketplace signals (e.g., ETA predictions, supply availability metrics, demand forecasts).
</li><li>Collaborate with cross-functional teams (Pricing, Matching, Driver Incentives, etc.) to ensure marketplace signals are effectively utilized.
</li><li>Improve operational efficiency by building a centralized, scalable system for marketplace signals that serves multiple use cases.
</li><li>Ensure consistency and reliability across Uber's platform by maintaining high-quality marketplace signals that inform rider and driver experiences.
</li><li>Reduce technical debt by streamlining signal infrastructure and minimizing redundant computations.
</li><li>Leverage cutting-edge ML techniques (deep learning, probabilistic modeling, reinforcement learning, etc.) to continuously refine marketplace signals.
</li><li>Work with real-time streaming data and large-scale distributed systems to ensure Uber's signals are up-to-date and responsive to market dynamics.
</li></ul>Basic Qualifications:<ul><li>
Ph.D. or M.S. in Statistics, Economics, Mathematics, Computer Science, Machine Learning, Operations Research, or other quantitative fields.
</li><li>6+ years of industry experience in machine learning, including building and deploying ML models at scale.
</li><li>Experience in modern deep learning architectures and probabilistic modeling
</li><li>Proficiency in programming languages (Python, Java, Scala) and ML frameworks (TensorFlow, PyTorch, Scikit-Learn),
</li><li>Solid understanding of MLOps practices, including design documentation, testing, and source code management with Git.
</li><li>Advanced skills in the development and deployment of large-scale ML models and optimization algorithms
</li><li>Strong business and product sense: ability to shape vague questions into well-defined analyses and success metrics that drive business decisions.
</li></ul>Preferred Qualifications:<ul><li>
Expertise in developing causal inference methodologies, experimental designs, and advanced analytical methods.
</li><li>Strong experience in building a wide range of models (e.g. causal inference, optimization, ML) for business applications.
</li><li>Experience in algorithm development and rapid prototyping.
</li><li>Design, develop, and operationalize econometric models to assess challenging causal problems such as product incrementality and long-term value
</li><li>Propose, design, and analyze large scale online experiments and interpret the results to draw actionable conclusions.
</li><li>Ability to drive clarity on the best modeling solution for a business objective.
</li>Collaborate with cross-functional teams across disciplines such as product, engineering, and operations to drive system development end-to-end from generating ideas to productionizing. 
<li>
</li></ul> 
 
For San Francisco, CA-based roles: The base salary range for this role is USD$232,000 per year - USD$258,000 per year. For Sunnyvale, CA-based roles: The base salary range for this role is USD$232,000 per year - USD$258,000 per year. For all US locations, you will be eligible to participate in Uber's bonus program, and may be offered an equity award &amp; other types of comp. All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits. More details can be found at the following link https://jobs.uber.com/en/benefits.</div>

Staff Machine Learning Engineer

Zendesk

<div><h2 class="jobSectionHeader">Job Description
</h2>The Enterprise Machine Learning team drives organizational value through scalable ML solutions and data-driven insights, fundamentally changing how business decisions are made. We collaborate closely with stakeholders, applying the latest advances in machine learning, deep learning, and large language models (LLMs) to create highly impactful outcomes. Our commitment is to advance the state of AI, statistical modeling, and robust system design to enhance and expand our core business capabilities.
<h2 class="jobSectionHeader">Location
</h2>San Francisco, CA or Austin, TX (Hybrid)
Schedule: This is a hybrid role requiring an average of 2 days per week in-office, or as otherwise determined by the hiring manager
<h2 class="jobSectionHeader">Role Overview
</h2>As a Machine Learning Engineer, you will serve as a technical and strategic member within the team, driving the development and deployment of advanced data science and machine learning solutions&mdash;particularly those harnessing LLMs and deep learning. You will architect and scale ML systems, foster effective cross-functional collaborations, and ensure that business value is embedded in every technical decision. Your business acumen allows you to translate complex analytical approaches into actionable insights and stakeholder-friendly narratives, strengthening partnership and adoption across the enterprise.
<h2 class="jobSectionHeader">Key Responsibilities
</h2><ul><li>Drive the design, development, and deployment of advanced ML and AI solutions, with an emphasis on large language models (LLMs), deep learning architectures, and sophisticated statistical modeling.
</li><li>Build scalable, robust data science systems&mdash;from data ingestion, data curation, data modeling to algorithm development, model deployment and monitoring&mdash;meeting enterprise-grade performance, reliability, and compliance standards.
</li><li>Act as a subject matter expert, collaborating with data scientists, ML engineers, analysts, and business stakeholders to understand needs, define requirements, and deliver practical solutions with measurable business impact.
</li><li>Effectively articulate complex technical concepts to non-technical partners, bridging gaps between technical teams and business operations for maximum results.
</li><li>Drive adoption of best practices in MLOps, including CI/CD pipelines, containerization, orchestration, observability, and reproducibility.
</li><li>Oversee and enhance the integrity, security, and compliance of all data science workflows and contracts.
</li><li>Stay abreast of the latest industry advancements in ML, LLMs, deep learning, cloud data engineering, and MLOps solutions (AWS, Kubernetes, Snowflake, etc.).
</li><li>Fostering technical excellence and ensuring alignment with business objectives.
</li></ul><h2 class="jobSectionHeader">What We&rsquo;re Looking For
</h2>Education &amp; Experience:
<ul><li>3+ years&rsquo; experience in Data Science, Machine Learning, or a related field
</li><li>BA/BS in Computer Science, Data Science, or related discipline (advanced degree is highly preferred)
</li></ul>Technical Expertise:
<ul><li>Deep expertise in statistical modeling, machine learning, and deep learning (including practical experience with LLMs and transformers)
</li><li>Strong programming skills (Python preferred; Java, Scala, or similar also valued)
</li><li>Proven ability to build and optimize scalable data science solution, end-to-end from data pipelines (dbt, Astronomer, Snowflake, AWS) to deployment and monitoring (Docker, Kubernetes, CI/CD, MLOps best practices)
</li><li>Experience handling and analyzing large datasets, with a preference for experience in cloud data warehouses (Snowflake)
</li></ul>Business Acumen:
<ul><li>Demonstrated success in translating business needs into analytical solutions, driving quantifiable impact
</li><li>Strong stakeholder engagement skills, with a track record of building trusted business partnerships and driving adoption of data science initiatives
</li></ul>Communication &amp; Collaboration:
<ul><li>Exceptional ability to simplify and communicate complex data science concepts to technical and non-technical audiences alike
</li><li>Experience working cross-functionally with engineers, analysts, and product leaders
</li><li>Steadfast commitment to continuous learning, collaboration, and fostering an inclusive, innovative team environment
</li></ul><h2 class="jobSectionHeader">Why You&rsquo;ll Thrive Here
</h2><ul><li>Opportunity to develop and scale of LLM and deep learning solutions with real-world business impact
</li><li>An environment that values innovation, ownership, and professional growth
</li><li>The chance to work on high-visibility, high-impact projects at scale alongside a passionate multidisciplinary team
</li></ul>The US annualized base salary range for this position is $206,000.00-$308,000.00. This position may also be eligible for bonus, benefits, or related incentives. While this range reflects the minimum and maximum value for new hire salaries for the position across all US locations, the offer for the successful candidate for this position will be based on job related capabilities, applicable experience, and other factors such as work location. Please note that the compensation details listed in US role postings reflect the base salary only (or OTE for commissions based roles), and do not include bonus, benefits, or related incentives.
Hybrid: In this role, our hybrid experience is designed at the team level to give you a rich onsite experience packed with connection, collaboration, learning, and celebration - while also giving you flexibility to work remotely for part of the week. This role must attend our local office for part of the week. The specific in-office schedule is to be determined by the hiring manager.
The intelligent heart of customer experience
Zendesk software was built to bring a sense of calm to the chaotic world of customer service. Today we power billions of conversations with brands you know and love.
Zendesk believes in offering our people a fulfilling and inclusive experience. Our hybrid way of working, enables us to purposefully come together in person, at one of our many Zendesk offices around the world, to connect, collaborate and learn whilst also giving our people the flexibility to work remotely for part of the week.
As part of our commitment to fairness and transparency, we inform all applicants that artificial intelligence (AI) or automated decision systems may be used to screen or evaluate applications for this position, in accordance with Company guidelines and applicable law.
Zendesk is an equal opportunity employer, and we&rsquo;re proud of our ongoing efforts to foster global diversity, equity, &amp; inclusion in the workplace. Individuals seeking employment and employees at Zendesk are considered without regard to race, color, religion, national origin, age, sex, gender, gender identity, gender expression, sexual orientation, marital status, medical condition, ancestry, disability, military or veteran status, or any other characteristic protected by applicable law. We are an AA/EEO/Veterans/Disabled employer. If you are based in the United States and would like more information about your EEO rights under the law, please click here .
Zendesk endeavors to make reasonable accommodations for applicants with disabilities and disabled veterans pursuant to applicable federal and state law. If you are an individual with a disability and require a reasonable accommodation to submit this application, complete any pre-employment testing, or otherwise participate in the employee selection process, please send an e-mail to peopleandplaces@zendesk.com with your specific accommodation request.</div>

Senior Machine Learning Engineer - Hybrid (San Francisco or Austin)

Bot Auto

<div><div><h3 class="jobSectionHeader">Company Introduction</h3>

At Bot Auto, we are revolutionizing the transportation of goods with our cutting-edge autonomous trucks, enhancing the quality of life for communities around the globe. With the agility of a start-up and the wisdom of seasoned experts, Bot Auto boasts a team that has achieved numerous world-firsts and unparalleled innovations. United by a shared vision, we create miracles and propel the future of transportation. Join us and transform your dreams into reality.
</div>
<h3 class="jobSectionHeader">Key Responsibilities</h3>
<ul><li>Explore and propose new ideas using your knowledge and experience in deep learning, neural networks and large foundation models in autonomous driving including: end-to-end object detection, tracking and prediction, end-to-end planning and control, and end-to-end autonomous driving system, end-to-end online mapping. SLAM in localization and etc..</li>
<li>Work on the entire life cycle of machine learning projects from data analysis, model experimentations to performance metrics verifications, and understand the entire workflow in great detail.</li>
<li>Be exposed to many cross-team projects and collaborate with the product, simulation and other sibling autonomous driving algorithm teams to extend machine learning technology to all components.</li>
</ul><h3 class="jobSectionHeader">Qualifications</h3>
<h3 class="jobSectionHeader">Required:</h3>
<ul><li>Have an advanced degree (Ph.D or Master's) in related fields of study: computer science, computer engineering, robotics, mathematics, physics, and etc.</li>
<li>Have in-depth knowledge and extensive experience in machine learning, and/or computer vision, modern transformer architecture, and employ SOTA techniques of machine learning.</li>
<li>Be familiar with PyTorch, TensorFlow and other machine learning platforms and tools.</li>
<li>Have strong motivation to work independently in a fast paced environment while collaborating with other teams on more complex and larger projects.</li>
</ul><h3 class="jobSectionHeader">Preferred:</h3>
<ul><li>Have a proven track record of research publications in top conferences and/or journals as the first author.</li>
<li>Have knowledge and experience of generative models, model distillation, or model inference acceleration (e.g. TensorRT) techniques.</li></ul></div>

Senior Machine Learning Engineer, Computer Vision, HD Map and SLAM

Explore 168+ Learning engineer in San Francisco this April. Find roles across top companies and apply now to boost your career.