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About This Role
Primer exists to make the world a safer place. We do this by providing trusted decision\-ready AI to the world's most critical organizations. Our software enables leaders, operators, and analysts to better understand the changing world around us in real time and make informed decisions when the stakes are high. Primer has offices in San Francisco, Pasadena, CA and Arlington, VA. For more information, please visit https://primer.ai/
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 primer.ai.
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
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- 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
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- 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
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- 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.
*Primer works closely with the U.S. defense and intelligence establishment. Any offer of employment is conditioned on an applicant or employee being able to meet any applicable government contract requirements. The company may rescind any offer of employment to an applicant or terminate an employee if the applicant or employee is unable to perform the functions of the position in compliance with applicable government contracts or if an applicant or employee makes a false attestation of compliance.*
What We Offer
We are a series D funded company with investors from Addition, USIT, Lux Capital, Amplify Partners, Addition Capital, Bloomberg Beta, and others. We are intentional around building a diverse and inclusive team of subject matter experts to better advocate for the needs of our users.
We care a lot about our work and about the well being of our team. We encourage everyone to work at a sustainable pace and have a flexible vacation policy for team members to utilize, Wellness Days and 100% paid leave for parents of growing families.
We offer competitive compensation and comprehensive benefits. This includes full medical, dental, and vision coverage, fertility benefits through Carrot, mental health coverage on demand with Headspace Care\+, Gympass\+ Membership via Wellhub, One Medical Membership, 401(k), remote work stipends, and monthly internet allowance.
Primer is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. Please see the United States Department of Labor's EEO poster and EEO poster supplement for additional information.
If you need assistance or accommodation due to a disability, you may contact us at [email protected].
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Primer.ai, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Primer.ai AI Hiring
Primer.ai has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Washington, DC, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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