Engineering Manager, Research Productivity
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 Anthropic is looking for an Engineering Manager to lead the Search Platform team. This team builds the search stack behind Claude: the indexes, retrieval and ranking systems and serving infrastructure that power web search across claude.ai , the API and agentic surfaces. Search is core to how Claude answers questions about the world, and the team owns both the problem and the solution end to end: growing the index, improving retrieval quality and running the serving stack at production scale. The same platform also serves research workloads, so demand comes from multiple user surfaces, often at the same time. The role carries a product dimension. Search sits close to the user experience and PM coverage is thin at times, so often the EM helps decide what a good search experience looks like, not just how to build it. You'll partner with product teams shipping search-backed features, research teams that depend on retrieval quality and capacity, and the infrastructure teams that run the systems underneath. We're looking for someone with real search experience who can be hands-on when needed: reviewing designs, digging into relevance regressions and holding their own in technical debates. Key Responsibilities - Lead and grow the team of engineers building Anthropic's search platform: indexing, retrieval, ranking and serving - Own the strategy and roadmap for the platform and how Claude's search needs are met over time - Own search quality: evaluation methodology, relevance measurement, regression detection and the ranking improvements they drive - Operate the platform at scale, balancing product traffic against research and training demand while holding a high bar on reliability, latency and cost - Wear the product hat when the work calls for it: prioritize what the search experience needs, sequence launches and represent search in product discussions - Drive cross-team collaboration with product, research and infrastructure partners; articulate dependencies, risks and progress clearly - Be hands-on where it matters: design reviews, incident follow-ups and the occasional deep dive into why relevance moved - Recruit, close and retain strong engineers in a competitive market Minimum Qualifications - Significant experience managing engineering teams, including hiring and growing a team through rapid change - Direct experience building or operating search systems at scale: indexing, retrieval, ranking or query serving - Enough technical depth to be hands-on when needed; you can review designs, read code and engage credibly with senior ICs - A product mindset and comfort making product calls when there isn't a PM in the room - A track record of running high-scale, latency-sensitive production systems with real reliability requirements - Strong cross-functional skills; you can align product, research and infrastructure partners with different priorities - Experience recruiting and closing senior engineers - Interest in AI safety and Anthropic's mission Preferred Qualifications - Experience with the economics of search: index freshness, storage and serving costs, quality vs cost tradeoffs - Background in embeddings, ranking models or ML-based retrieval - Experience migrating traffic off a vendor onto in-house infrastructure - Exposure to LLM products and the retrieval demands of large-scale training and inference 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: $320,000 — $405,000 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
Machine 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 Engineering Manager, Research Productivity posting at a beginner level and can be completed in approximately 1 hours.
Leverage AI tools and resources for your business
Microsoft Learn · beginner · Free · 42m · ★ 4.8
This course covers Machine Learning, which appears as a requirement in the Engineering Manager, Research Productivity posting at a beginner level and can be completed in approximately 1 hours.
Intro to Machine Learning
Kaggle Learn · beginner · Free
This course directly covers Machine Learning, which appears as a requirement in the Engineering Manager, Research Productivity posting at a beginner level.
Team Leadership (required)
GapCourses that may help you meet this requirement:
Civic Media Codesign Studio
MIT Learn · intermediate · Free
This course covers Team Leadership, which appears as a requirement in the Engineering Manager, Research Productivity posting at an intermediate level.
Leading Teams
MIT Learn · $1100
This course directly addresses the Team Leadership skill gap identified for the Engineering Manager, Research Productivity role, providing relevant training to strengthen this competency.
Systems Leadership and Management Praxis
MIT Learn · advanced · Free
This course directly covers Team Leadership, which appears as a requirement in the Engineering Manager, Research Productivity posting at an advanced level.
Incident Response (required)
GapCourses that may help you meet this requirement:
Improve your reliability with modern operations practices: Incident response
Microsoft Learn · beginner · Free · 33m · ★ 4.7
This beginner course directly addresses the Incident Response skill gap for the Engineering Manager, Research Productivity role by covering core incident and response practices in a quick 0.55-hour session.
Describe the core features of Microsoft Security Copilot
Microsoft Learn · beginner · Free · 1h
This beginner course introduces core features of Microsoft Security Copilot, but since no matched keywords link it directly to Incident Response, it may only partially address the skill gap for the Engineering Manager, Research Productivity role.
Build multi-table statements using KQL
Microsoft Learn · intermediate · Free · 21m · ★ 4.7
This course is recommended to help build KQL querying skills, though it does not directly cover Incident Response content needed for the Engineering Manager, Research Productivity role based on available data.
People Management (required)
GapCourses that may help you meet this requirement:
Business and Impact Planning for Social Enterprises (0.SolveX)
MIT Learn · beginner · Free
This course directly covers People Management, which appears as a requirement in the Engineering Manager, Research Productivity posting at a beginner level.
Practical Information Technology Management
MIT Learn · intermediate · Free
This intermediate course covers management, directly building the People Management skill needed for the Engineering Manager, Research Productivity role.
Law for the Entrepreneur and Manager
MIT Learn · intermediate · Free
This course is recommended to help address the People Management skill gap for the Engineering Manager, Research Productivity role, though no specific matched keywords or topics were provided to confirm direct coverage of that skill.
Project Management (required)
GapCourses that may help you meet this requirement:
Proforma invoicing and corrections in Dynamics 365 Project Operations
Microsoft Learn · beginner · Free · 1h · ★ 5.0
This beginner course covers project and management concepts relevant to the Project Management skill gap identified for the Engineering Manager, Research Productivity role.
Get started with project management in Dynamics 365 Project Operations
Microsoft Learn · intermediate · Free · 2h · ★ 4.7
This intermediate course directly builds Project Management skills through its focus on project and management fundamentals in Dynamics 365, addressing a key skill gap for the Engineering Manager, Research Productivity role.
Project Management
MIT Learn · intermediate · Free
This intermediate Project Management course directly builds the project and management skills needed to close the Project Management gap for the Engineering Manager, Research Productivity role.
Fine-Tuning (required)
GapCourses that may help you meet this requirement:
Hugging Face NLP Course
Hugging Face Learn · intermediate · Free
This course directly covers Fine-Tuning, which appears as a requirement in the Engineering Manager, Research Productivity posting at an intermediate level.
Optimize and fine-tune AI agents for production
Microsoft Learn · intermediate · Free · 1.5h
This intermediate course directly addresses the Fine-Tuning skill gap by covering fine tuning techniques needed to optimize AI agents for production, making it relevant for the Engineering Manager, Research Productivity role.
Distributed Systems (required)
GapETL (required)
GapCourses that may help you meet this requirement:
Explore data roles and services
Microsoft Learn · beginner · Free · 22m · ★ 4.8
This course covers ETL, which appears as a requirement in the Engineering Manager, Research Productivity posting at a beginner level and can be completed in approximately 0 hours.
Explore fundamentals of large-scale analytics
Microsoft Learn · beginner · Free · 51m · ★ 4.8
This course covers ETL, which appears as a requirement in the Engineering Manager, Research Productivity posting at a beginner level and can be completed in approximately 1 hours.
Execute existing SSIS packages in Azure Data Factory or Azure Synapse Pipeline
Microsoft Learn · intermediate · Free · 24m · ★ 4.5
This course covers ETL, which appears as a requirement in the Engineering Manager, Research Productivity posting at an intermediate level and can be completed in approximately 0 hours.
Agentic Workflows (required)
GapLarge Language Models (required)
GapAI Safety (required)
GapEmbeddings (required)
GapDistributed Training (required)
Gap