Model Performance Software Engineer, Claude Code
Anthropic · San Francisco, CA | New York City · Onsite
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Anthropic
Anthropic is an AI safety and research company founded in 2021 by former OpenAI executives, including siblings Dario and Daniela Amodei, who left to pursue a more safety-focused approach to building powerful AI systems. Its flagship product, the Claude family of large language models, competes directly with OpenAI's GPT series and Google's Gemini, and has become widely used in enterprise and developer settings, particularly for coding and reasoning tasks. The company positions itself distinctly within the AI industry by emphasizing interpretability and alignment research alongside commercial deployment, framing itself as a lab racing to build capable frontier models while trying to ensure they remain steerable and beneficial as capabilities scale.
Gig description
About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role As a Product Manager on Claude Code's model performance team, you will drive model launches end-to-end, build evals that measure what matters, and partner directly with researchers and product engineers to translate model improvements into developer-facing outcomes. Claude Code is the most capable coding agent in the world but there’s much more we can do to extract the maximum performance from our models. We're looking for a PM who has personally built agentic evals, thinks in systems, uses Claude Code every day, and has refined model taste. You should be as comfortable influencing our research team as you are getting in the weeds of transcripts. You will be the connective tissue between frontier research and the millions of developers who depend on Claude Code to do their best work. Responsibilities - Own model launch planning and execution for Claude Code: define readiness criteria, coordinate across research and product engineering, and ensure launches land cleanly with developers - Design and implement agentic evals that measure real-world coding performance - Drive the engineering team's eval roadmap - Partner with researchers working on coding capabilities to define target behaviors and influence model development with evidence from real usage - Talk with users and analyze transcripts to understand capability gaps and turn research progress into shipped improvements - Synthesize signal from internal users, external developers, and competitive benchmarks into clear priorities You might be a good fit if you - Have personally built agentic evals (e. g. SWE-bench-style task suites) - Are a daily Claude Code user and can articulate what behaviors you’d want to change or add to the model - Have an engineering background and 2+ years in product management, or equivalent experience driving product direction as an engineer - Have a deep grasp of AI concepts and are comfortable going deep on model behavior, prompt engineering, and evaluation methodology - Are a systems thinker: when you find a problem, you build the infrastructure that prevents its whole class - Have launched products or capabilities in ambiguous, research-adjacent environments - Have a creative, hacker spirit and love solving puzzles San Francisco and Seattle only The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $305,000 — $460,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.
Requirements to meet
Disclaimer
Suggestions only — review each course yourself to judge whether it meets the role's requirements. Completing a course doesn't guarantee proficiency or that you'll qualify; hiring standards vary by employer.
Skills required
Model Evaluation (required)
GapMachine Learning (required)
GapCourses that may help you meet this requirement:
Introduction to DevOps principles for machine learning
Microsoft Learn · beginner · Free · 33m · ★ 4.7
This course directly covers Machine Learning, which appears as a requirement in the Model Performance Software Engineer, Claude Code posting at a beginner level and can be completed in approximately 1 hours.
Continuous deployment for machine learning
Microsoft Learn · beginner · Free · 22m · ★ 4.7
This course directly covers Machine Learning, which appears as a requirement in the Model Performance Software Engineer, Claude Code posting at a beginner level and can be completed in approximately 0 hours.
Intro to Machine Learning
Kaggle Learn · beginner · Free
This course directly covers Machine Learning, which appears as a requirement in the Model Performance Software Engineer, Claude Code posting at a beginner level.
Agentic Workflows (required)
GapCourses that may help you meet this requirement:
Understand features of Copilot Studio agents
Microsoft Learn · beginner · Free · 54m
This course covers Agentic Workflows, which appears as a requirement in the Model Performance Software Engineer, Claude Code posting at a beginner level and can be completed in approximately 1 hours.
Design ALM process for AI-powered business solutions
Microsoft Learn · intermediate · Free · 46m
This course covers Agentic Workflows, which appears as a requirement in the Model Performance Software Engineer, Claude Code posting at an intermediate level and can be completed in approximately 1 hours.
Hugging Face Agents Course
Hugging Face Learn · intermediate · Free
This course covers Agentic Workflows, which appears as a requirement in the Model Performance Software Engineer, Claude Code posting at an intermediate level.
Python (required)
GapCourses that may help you meet this requirement:
Data Analysis with Python
freeCodeCamp · beginner · Free
This course directly covers Python, which appears as a requirement in the Model Performance Software Engineer, Claude Code posting at a beginner level.
Pandas
Kaggle Learn · beginner · Free
This beginner Pandas course is recommended to help build foundational Python skills needed for the Model Performance Software Engineer, Claude Code role.
CS50's Introduction to Artificial Intelligence with Python
CS50 · intermediate · Free
This course directly covers Python, which appears as a requirement in the Model Performance Software Engineer, Claude Code posting at an intermediate level.
TypeScript (required)
GapReinforcement Learning (required)
GapCourse that may help you meet this requirement:
Machine Learning with Python: from Linear Models to Deep Learning
MIT Learn · Free
This course covers reinforcement learning concepts, directly addressing the Reinforcement Learning skill gap required for the Model Performance Software Engineer, Claude Code role.
Prompt Engineering (required)
GapProduct Management (required)
Gap