Staff-Machine-Learning-Engineer, Machine-Learning-Engineer, AI-Engineer Jobs

Our Retrieval team builds shared machine learning capabilities that help Pinterest connect people with relevant ideas. We are looking for a Staff Machine Learning Engineer to advance how we develop those capabilities through agentic AI.

Your primary focus will be Reflex, our effort to automate and accelerate recommendation research and development. You will own a major technical area, building agents that investigate opportunities, implement changes, run experiments and learn from their results. You will work closely with other technical leaders while remaining hands-on with the hardest engineering and modeling problems.

You will also contribute to generative retrieval, with a particular focus on Semantic IDs: learning structured representations of content that recommendation models can generate and use to retrieve relevant items. This role combines applied AI research, production engineering and technical leadership, with opportunities to improve both our recommendation models and the way engineers build them.

What you'll do:

• Own and advance a major technical area in automated recommendation research within Reflex, collaborating with other technical leads and partner teams.
• Build LLM agents that connect hypothesis generation, implementation, experiment execution, evaluation and learning from previous results.
• Develop rigorous evaluations for agent behavior and research outputs, and use them to improve quality, reliability and efficiency.
• Contribute to generative retrieval models, particularly Semantic ID learning, evaluation and integration into production recommendation systems.
• Take promising ideas from research and prototypes through offline evaluation, online experimentation and production launches.
• Build reliable integrations with ML development infrastructure and make informed tradeoffs across model quality, iteration speed, compute cost and operational complexity.
• Raise technical quality through hands-on implementation, design and code reviews, documentation and mentorship.
• Work with research, recommendation and ML infrastructure teams to shape technical approaches, resolve dependencies and support adoption.
• Identify and apply relevant advances from research, and contribute new methods where existing approaches fall short.

What we're looking for:

• Bachelor's in Computer Science, Machine Learning or a related field, or equivalent practical experience.
• Minimum 8 years of experience building and shipping ML or AI systems, including substantial hands-on engineering and technical leadership.
• Demonstrated experience building LLM agents or other agentic AI systems for complex tasks, with a clear approach to evaluating and improving their performance.
• Strong ML fundamentals and depth in one or more relevant areas, such as applied LLMs, representation learning, recommendation systems, retrieval or ML systems.
• Experience taking research ideas into reliable production systems and evaluating their impact through meaningful experiments.
• Strong software engineering skills, including the ability to design maintainable systems, implement difficult components and conduct thorough technical reviews.
• Experience leading a substantial technical area, making decisions under ambiguity and collaborating effectively across teams.
• A track record of mentoring engineers and raising the quality of a team’s technical work.
• Clear written and verbal communication, including the ability to explain technical tradeoffs to different audiences.
• A disciplined approach to using AI in your own work, including verification of generated code, analysis and conclusions.

We'll teach you our stack and our products, and we're committed to your continued growth as a technical leader. This is an individual contributor role; prior people management isn't required.

Relocation Statement:

• This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.

In-Office Requirement Statement:

• We let the type of work you do guide the collaboration style. That means we're not always working in an office, but we continue to gather for key moments of collaboration and connection.
• This role will need to be in the office for in-person collaboration 1-2 times/quarter and therefore can be situated anywhere in the country.

Originally posted on Himalayas

Pay Information

GeoZone Definition: GeoZones are geographic groupings created by Lockheed Martin to align compensation ranges with regional labor markets and cost-of-labor differences across the United States. Locations are assigned a Geo Zone based on the primary work location of the role.

• Full-time salary range (GEOZONE 1): $150800.00 - $280000.00
  • Includes metropolitan areas such as Sunnyvale CA; Pal Alto, CA; New York City metropolitan area; Newark, New Jersey; etc.
• Full-time salary range (GEOZONE 2): $135700.00 - $251900.00
  • Includes metropolitan areas such as Denver, CO; King of Prussia, PA; Stratford, CT; Moorestown, NJ; etc.
• Full-time salary range (GEOZONE 3): $120600.00 - $224000.00
  • Includes metropolitan areas such as Dallas–Fort Worth, TX; Orlando, FL; Grand Prairie, TX; Marietta, GA; etc.
• Full-time salary range (GEOZONE 4): $108600.00 - $201600.00
  • Includes metropolitan areas such as Camden, AR; Lexington, KY; Lufkin, TX; etc.

At Lockheed Martin, we know mission success starts with taking care of our people. Our Total Rewards program is designed to attract top talent, support your well-being, and help you grow—both professionally and personally.

The salary range for this position is as listed on the requisition. Please note that the salary information listed is a general guideline only.
Lockheed Martin considers factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience, education/ training, key skills as well as market(work location) and business considerations when extending an offer.

Benefits offered: Medical, Dental, Vision, Flexible work arrangements and schedules (e.g., 4x10), 401(k) match, Paid time off, Holidays, Parental Leave, EAP, Flexible Spending Accounts, Education Assistance, Life Insurance, Short-Term Disability, and Long-Term Disability.

• Annual short-term and/or long-term incentive compensation programs may be offered depending on the position. Payments under these annual programs are not guaranteed and can vary from year to year and are tied to a range of performance metrics.
• For (Washington state applicants only) Non-represented full-time employees: accrue at least 10 hours per month of Paid Time Off (PTO) to be used for incidental absences and other reasons; receive at least 90 hours for holidays. Represented full time employees accrue 6.67 hours of Vacation per month; accrue up to 52 hours of sick leave annually; receive at least 96 hours for holidays. PTO, Vacation, sick leave, and holiday hours are prorated based on start date during the calendar year.

Originally posted on Himalayas

Primer exists to make the world a safer place. We build trusted, decision-ready AI for the organizations that can least afford to be wrong: the leaders, operators, and analysts making high-stakes calls about a world that changes faster than anyone can read it. We have offices in San Francisco, Pasadena, CA, and Arlington, VA. Learn more at .

The world produces more information every day than any team can read. We build the AI that turns it into a decision you can trust: fast and grounded enough to stake real consequences on.

As a Staff Machine Learning Engineer, you’ll own AI-driven products end to end, from the prototype that proves an idea works to the production system the mission depends on. You work fluently across the modern stack: large language models, agentic systems, retrieval, embeddings, fine-tuned models, and the evaluation harnesses that catch them when they drift. You can move fast on an open-ended problem and then harden the result into something reliable at scale, drawing on real distributed-systems experience. You’re energized by what frontier models and agents make newly possible. You reach for them reflexively, including to multiply your own output, and you raise the level of everyone around you as you go.

Your technical range matters as much as your judgment about how the pieces fit together. The best work here is designed as part of the whole system, not bolted on beside it, and the engineers with the most impact bring product managers and teammates along with them rather than around them. You’ll partner with product, engineering, and other technical leaders on the hardest version of the problem: putting real AI in the hands of people the moment a decision can’t wait.

Role and Responsibilities - How You Will Make an Impact

• Set both technical and product direction, making the strategic bets across our LLM, agentic, and NLP systems while shaping how we design AI-driven experiences and set expectations with users.
• Design and build the distributed, agentic systems behind our products at company-wide scale: tool-using conversational agents, multi-turn context, retrieval-grounded reasoning, and the orchestration that ties them together.
• Turn massive, messy, real-world data into the trustworthy signal agents' reason over: entity recognition and linking, relation extraction, summarization, semantic search, and knowledge-graph generation.
• Take models from checkpoint to production: package, deploy, and operate low-latency, high-concurrency inference (Triton, vLLM, GPU-backed serving) that stays fast and reliable under real load.
• Build the evals and labeled-data flywheels that steer the work rather than gate it, turning “it feels better” into proof you can act on.
• Partner with and influence cross-functional teams to shape the technical roadmap, and drive issues to root cause when quality or operations are on the line.
• Raise the engineering bar with better patterns, better practices, and a standard other engineers want to match.

Relevant Skills and Experience

• BS, MS, or PhD in computer science, a related field, or equivalent practical experience.
• 6+ years building production backend software, with a track record of shipping and operating ML-driven functionality.
• Mastery of data structures and algorithms, and the judgment to translate user needs into practical solutions.
• Hands-on depth with LLMs and agentic systems (prompt and context engineering, tool use, retrieval and RAG) and the broader ML toolkit such as PyTorch, plus experience defining evals to measure and improve quality.
• Fluency authoring production APIs in Python (Rust a plus).
• High agency and a bias to action in ambiguous, fast-moving problems, plus the curiosity, generosity, and love of teaching that lifts a whole team.

Bonus Points

• Experience with knowledge graphs, information retrieval at scale, or LLM fine-tuning and post-training.
• A pull toward high-stakes, real-world problems where the work actually ships and someone depends on the answer.

Originally posted on Himalayas