anthropic
Staff Machine Learning Engineer, Agent Skills
anthropic, Seattle, Washington, United States, 98127
Staff Machine Learning Engineer, Agent Skills
Remote‑Friendly (Travel‑Required) | San Francisco, CA | Seattle, WA | New York City, NY 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
Agentic systems are the next frontier of usefulness for Claude. Over the last year, we’ve seen rapid adoption of Claude‑powered agentic systems in spaces like coding, research, customer support, network security, and more. We believe this is just the beginning, and we expect Claude to be handling much more complex tasks end‑to‑end or in cooperation with a human user as time goes on. We have a team striving to make Claude an even more effective agent, focusing on planning, reliable execution over longer time‑horizon tasks, scaled tool use, Agent Skills, memory, and inter‑agent coordination. This team endeavors to maximize agent performance by solving challenges at whatever level is needed, whether it’s finetuning, agent infrastructure, or agent design best practices. Given that this is a nascent field, we ask that you share with us a project built on LLMs that showcases your skill at getting them to do complex tasks. Here are some example projects of interest: Design of complex agents Quantitative experiments with prompting Constructing model benchmarks Synthetic data generation Model finetuning Application of LLMs to a complex task Responsibilities
Finetune new capabilities into Claude that maximize Claude’s performance or ease of use on agentic tasks Ideate, develop, and compare the performance of different tools for agents (e.g., memory, context compression, communication architectures for agents) Develop and improve the virtual machine infrastructure and affordances that Claude has access to Systematically discover and test best practices for creating and using Agent Skills Develop automated techniques for designing and evaluating agentic systems Assist with automated evaluation of Claude models and prompts across the training and product lifecycle Work with our product org to find solutions to our most vexing challenges applying agents to our products Help create and optimize data mixes for model training You may be a good fit if you:
Have 7+ years of ML and software engineering experience Have at least a high‑level familiarity with the architecture and operation of large language models Have extensive prior experience exploring and testing language model behavior Have spent time prompting and/or building products with language models Have good communication skills and an interest in working with other researchers on difficult tasks Have a passion for making powerful technology safe and societally beneficial Stay up‑to‑date and informed by taking an active interest in emerging research and industry trends Enjoy pair programming (we love to pair!) Strong candidates may also have experience with:
Developing complex agentic systems using LLMs Large‑scale RL on language models Multi‑agent systems Representative projects
Implementing and testing a novel retrieval, tool use, sub‑agent, or memory architecture for Claude Finetuning Claude to maximize its performance using a particular set of agent tools (e.g., a read‑write memory or an inter‑agent communication system) Designing and validating pipelines for creating Agent Skills for specific use cases (e.g., financial analysis, code generation, document processing) with measurable performance improvements Building evaluation frameworks to test Agent Skills effectiveness across diverse agentic workflows, and identifying optimal Skills composition patterns Building and testing automated systems for Agent Skills creation Compensation
The expected base compensation for this position is below. Our total compensation package for full‑time employees includes equity, benefits, and may include incentive compensation. $340,000 – $560,000 USD Logistics
Education requirements:
We require at least a Bachelor's degree in a related field or equivalent experience. 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 can’t sponsor every role and every candidate. If we make you an offer, we will get every reasonable effort to get you a visa. 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. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. 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. We value impact — advancing our long‑term goals of steerable, trustworthy AI — more than working 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. 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. Equal Employment Opportunity
As set forth in Anthropic’s Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.
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Remote‑Friendly (Travel‑Required) | San Francisco, CA | Seattle, WA | New York City, NY 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
Agentic systems are the next frontier of usefulness for Claude. Over the last year, we’ve seen rapid adoption of Claude‑powered agentic systems in spaces like coding, research, customer support, network security, and more. We believe this is just the beginning, and we expect Claude to be handling much more complex tasks end‑to‑end or in cooperation with a human user as time goes on. We have a team striving to make Claude an even more effective agent, focusing on planning, reliable execution over longer time‑horizon tasks, scaled tool use, Agent Skills, memory, and inter‑agent coordination. This team endeavors to maximize agent performance by solving challenges at whatever level is needed, whether it’s finetuning, agent infrastructure, or agent design best practices. Given that this is a nascent field, we ask that you share with us a project built on LLMs that showcases your skill at getting them to do complex tasks. Here are some example projects of interest: Design of complex agents Quantitative experiments with prompting Constructing model benchmarks Synthetic data generation Model finetuning Application of LLMs to a complex task Responsibilities
Finetune new capabilities into Claude that maximize Claude’s performance or ease of use on agentic tasks Ideate, develop, and compare the performance of different tools for agents (e.g., memory, context compression, communication architectures for agents) Develop and improve the virtual machine infrastructure and affordances that Claude has access to Systematically discover and test best practices for creating and using Agent Skills Develop automated techniques for designing and evaluating agentic systems Assist with automated evaluation of Claude models and prompts across the training and product lifecycle Work with our product org to find solutions to our most vexing challenges applying agents to our products Help create and optimize data mixes for model training You may be a good fit if you:
Have 7+ years of ML and software engineering experience Have at least a high‑level familiarity with the architecture and operation of large language models Have extensive prior experience exploring and testing language model behavior Have spent time prompting and/or building products with language models Have good communication skills and an interest in working with other researchers on difficult tasks Have a passion for making powerful technology safe and societally beneficial Stay up‑to‑date and informed by taking an active interest in emerging research and industry trends Enjoy pair programming (we love to pair!) Strong candidates may also have experience with:
Developing complex agentic systems using LLMs Large‑scale RL on language models Multi‑agent systems Representative projects
Implementing and testing a novel retrieval, tool use, sub‑agent, or memory architecture for Claude Finetuning Claude to maximize its performance using a particular set of agent tools (e.g., a read‑write memory or an inter‑agent communication system) Designing and validating pipelines for creating Agent Skills for specific use cases (e.g., financial analysis, code generation, document processing) with measurable performance improvements Building evaluation frameworks to test Agent Skills effectiveness across diverse agentic workflows, and identifying optimal Skills composition patterns Building and testing automated systems for Agent Skills creation Compensation
The expected base compensation for this position is below. Our total compensation package for full‑time employees includes equity, benefits, and may include incentive compensation. $340,000 – $560,000 USD Logistics
Education requirements:
We require at least a Bachelor's degree in a related field or equivalent experience. 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 can’t sponsor every role and every candidate. If we make you an offer, we will get every reasonable effort to get you a visa. 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. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. 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. We value impact — advancing our long‑term goals of steerable, trustworthy AI — more than working 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. 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. Equal Employment Opportunity
As set forth in Anthropic’s Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.
#J-18808-Ljbffr