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About This Role
Job Description – Member of Technical Staff (Applied AI Research)
Location: San Francisco (on\-site at our offices)
About Artificial Analysis
Artificial Analysis is the leading independent AI benchmarking company. We support labs, engineers and enterprises to understand AI capabilities and make critical decisions about their AI strategies. We are the go\-to authority for understanding AI, from AI labs and enterprises to media, investors, and policymakers. Our benchmarks don’t just measure the cutting edge of AI, they are actively shaping the frontier.
Our benchmarks and analysis are trusted by hundreds of thousands of users and are the go\-to reference for leading AI labs including OpenAI, Google, Meta, NVIDIA and Anthropic, and major publications including the Wall Street Journal, Bloomberg, the Financial Times and The Economist.
We are a team of 40\+, on track to double by end of year, backed by Nat Friedman (GitHub, Meta), Daniel Gross (SSI, Meta), Andrew Ng (Google Brain, DeepLearning.ai, Amazon), Adam D’Angelo (Quora, Poe, OpenAI), Clem Delangue (Hugging Face) and other industry leaders.
The Opportunity
Our evaluations decide how the world measures AI. When a frontier lab ships a model, our benchmarks are how the industry finds out what it can actually do, and the labs themselves use our results to guide what they build next. This role puts you on the measurement frontier: you won’t just observe the cutting edge, your work will define what cutting edge means.
We’re hiring Members of Technical Staff (Applied AI Research) to design the evaluations that set the standard for how AI is measured: building novel benchmarks and datasets, evaluating every major model as it releases, working with the frontier labs on pre\-release models before the world sees them, and publishing the analysis that labs, enterprises, media and policymakers rely on. The bar for success is becoming a world expert in modern AI.
This is applied research with immediate industry consequence: shorter cycles than academia, more rigor than industry commentary, and a bigger audience than both. The center of the role is building: the large majority of your time goes to designing and shipping evaluations, with analysis and industry collaboration built around that work.
What You’ll Do
- Design Frontier Evaluations: Conceive, build and ship novel evaluation methodologies that advance how AI capabilities are measured, and that stay ahead of what frontier models can do
- Build Evaluation Datasets and Infrastructure: Construct the datasets, harnesses and scoring systems behind our benchmarks, engineered for contamination resistance and repeatability at frontier scale
- Evaluate Every Major Model: Run our evaluation suite across frontier releases as they land, and own the integrity of the results the industry quotes
- Publish Influential Analysis: Produce the reports, indexes and data visualizations that shape how labs, enterprises and the broader industry understand AI progress
- Work with Frontier Labs on Pre\-Release Models: Benchmark the leading labs’ systems, including pre\-release and newly launched models, working directly with their research teams; our commercial team owns client relationships day to day, so your time stays on the science
- Become AI\-Native: Embrace an AI\-native workflow, using cutting\-edge AI tools to generate leverage in a fast\-changing industry and maintain our competitive edge in AI benchmarking
What We’re Looking For
We require 3\+ years of relevant professional experience, across industry or research. You have an intense interest in AI, a desire to become a world expert in the field, and strong analytical and coding skills to back it up.
Beyond that bar, we hire from three backgrounds. You should clearly fit one of these profiles:
AI and Machine Learning
Backgrounds include: ML Engineer, ML Researcher, Research Engineer, AI Engineer, Forward Deployed Engineer, Technical PM, or similar roles at AI companies or AI\-focused teams.
You have hands\-on experience with modern AI systems and understand how models work at a technical level. You want your work to ship faster than a paper and matter more than a dashboard: evaluations the whole industry sees, on a cycle measured in days.
Strategy Consulting
Backgrounds include: Management Consultant, Associate, Engagement Manager, Data Scientist, or similar roles at firms like McKinsey, BCG, Bain, or equivalent. Strong preference will be given to candidates with experience within a Data Analytics division such as QuantumBlack, AI by McKinsey, BCG X or equivalent.
You know how to structure ambiguous problems, build analytical frameworks, and communicate findings to senior stakeholders. The key differentiator: genuine technical interest in AI and the ability to code. You follow model releases, you have opinions on where the technology is heading, and you want to be closer to the subject matter than consulting allows.
Technical Product Management
Backgrounds include: Founding Engineer, Product Manager, Technical Co\-founder, Head of Product, or generalist roles at early\-stage AI companies.
You’ve built and shipped AI products in a fast\-moving environment, operating across research, engineering, product and commercial work simultaneously. You’re looking for a role where that breadth is the job, not a side effect of being early at a small company.
Across all three profiles, we require:
- Strong analytical and critical thinking skills
- Proficiency in Python and data analysis
- Genuine, demonstrable interest and knowledge of Frontier AI. We want people who have informed opinions about where AI is heading, not just people who use AI tools
Why Artificial Analysis?
- Shape how AI gets built: The leading AI labs track our benchmarks and use them to guide their development priorities. Your work will directly influence the direction of AI.
- Become a world expert in AI: You will evaluate every major model, across every major capability, as they are released. Very few roles offer this breadth of exposure to frontier AI.
- Work with the most important players in AI: You’ll manage relationships with teams at the leading AI labs and major enterprises as a trusted, independent voice.
- Join at a defining moment: We’re 40\+ people, on track to double by end of year, backed by some of the most connected investors in AI. The people who join now will shape the product, the team, and the strategy as we scale.
- Competitive compensation including equity
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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 Artificial Analysis, 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.
Artificial Analysis AI Hiring
Artificial Analysis has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US.
Location Context
AI roles in San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% above the national 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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