Research Engineer, Universes
AnthropicRemote-Friendly (Travel-Required) | San Francisco, CA | Seattle, WA | New York City, NYPosted 7 April 2026
Tech Stack
Job 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 Team
The Universes team within Research is responsible for training AI models to perform complex, difficult, long-horizon agentic tasks in ultra-realistic settings. We design and implement novel training environments that go far beyond what models can do today — environments where models learn to navigate ambiguity, handle interruptions, maintain context over extended interactions, and exercise judgment in open-ended scenarios.
About the Role
We're looking for Research Engineers to help us build the next generation of training environments for capable and safe agentic AI.
This role blends research and engineering responsibilities, requiring you to both implement novel approaches and contribute to research direction. You'll work on fundamental research in reinforcement learning, designing training environments and methodologies that push the state of the art, and building evaluations that measure genuine capability.
Responsibilities:
Build the next generation of agentic environments
Build rigorous evaluations that measure real capability
Collaborate across research and infrastructure teams to ship environments into production training
Debug and iterate rapidly across research and production ML stacks
Contribute to research culture through technical discussions and collaborative problem-solving
You may be a good fit if you:
Are highly impact-driven — you care about outcomes, not activity
Operate with high agency
Have good research taste or senior technical experience, demonstrating good judgment in identifying what actually matters in complex problem spaces
Can balance research exploration with engineering implementation
Are passionate about the potential impact of AI and are committed to developing safe and beneficial systems
Are comfortable with uncertainty and adapt quickly as the landscape shifts
Have strong software engineering skills and can build robust infrastructure
Enjoy pair programming (we love to pair!)
Strong candidates may also have one or more of the following:
Have industry experience with large language model training, fine-tuning or evaluation
Have industry experience building RL environments, simulation systems, or large-scale ML infrastructure
Senior experience in a relevant technical field even if transitioning domains
Deep expertise in sandboxing, containerization, VM infrastructure, or distributed systems
Published influential work in relevant ML areas
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:
$500,000
$850,000 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 ... (truncated, view full listing at source)
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