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.
Our team is organized around the north star goal of building an AI scientist – a system capable of solving the long term reasoning challenges and basic capabilities necessary to push the scientific frontier. Our team likes to think across the whole model stack. Currently the team is focused on improving models' abilities to use computers – as a laboratory for long horizon tasks and a key blocker to many scientific workflows.
About the roleAs a Research Engineer on our team you will work end to end, identifying and addressing key blockers on the path to scientific AGI. Strong candidates should have familiarity with language model training, evaluation, and inference, be comfortable triaging research ideas and diagnosing problems and enjoy working collaboratively. Familiarity with performance optimization, distributed systems, vm/sandboxing/container deployment, and large scale data pipelines is highly encouraged.
Join us in our mission to develop advanced AI systems that are both powerful and beneficial for humanity.
Responsibilities:- Working across the full stack to identify and remove bottlenecks preventing progress toward scientific AGI
- Develop approaches to address long-horizon task completion and complex reasoning challenges essential for scientific discovery
- Scaling research ideas from prototype to production
- Create benchmarks and evaluation frameworks to measure model capabilities in scientific workflows and computer use
- Implement distributed training systems and performance optimizations to support large-scale model development
- Have 8+ years of ML research experience
- Are familiar with large scale language model training, evaluation, and inference pipelines
- Enjoy obsessively iterating on immediate blockers towards longterm goals
- Thrive working collaboratively to solve problems
- Have expertise in performance optimization and distributed computing systems
- Show strong problem-solving skills and ability to identify technical bottlenecks in complex systems
- Can translate research concepts into scalable engineering solutions
- Have a track record of shipping ML systems that tackle challenging multi-step reasoning problems
- Expertise with performance optimization for language model inference and training
- Experience with computer use automation and agentic AI systems
- A history working on reinforcement learning approaches for complex task completion
- Knowledge of containerization technologies (Docker, Kubernetes) and cloud deployment at scale
- Demonstrated ability to work across multiple domains (language modeling, systems engineering, scientific computing)
- Have experience with VM/sandboxing/container deployment and large-scale data processing
- Experience working with large scale data problem solving and infrastructure
- Published research or practical experience in scientific AI applications or long-horizon reasoning
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.
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 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.
How we're differentWe 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
Top Skills
What We Do
Anthropic is an AI safety and research company that’s working to build reliable, interpretable, and steerable AI systems. Our research interests span multiple areas including natural language, human feedback, scaling laws, reinforcement learning, code generation, and interpretability.
