Research Engineer, Universes
Remote-Friendly (Travel-Required) | San Francisco, CA | Seattle, WA | New York City, NYFull-TimeMid-levelSoftware Engineering
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
- 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!)
- 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
- 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.
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.
