Staff Research Engineer, Discovery Team
Anthropic · San Francisco, CA · AI Research & Engineering · listed April 23, 2025
The shape of it
Seniority
Staff
Experience asked
8+ years
Where
Hybrid
Stated pay
$350,000 – $850,000 USD
Requirements listed
8
Length
1,033 words
In the posting’s own words
As 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.
What it asks for · 8
- 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
Also a plus
- 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
What the job covers
- 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
Tools and skills named
Models & research
- Inference3×
- Machine learning2×
- Reinforcement learning
Cloud & infra
- Distributed systems
- Docker
- Kubernetes
Data
- Data pipelines
Words the posting leans on
- systems9×
- scientific8×
- model7×
- research6×
- experience5×
- problems4×
- reasoning4×
- training4×
- blockers3×
- complex3×
- computer3×
- distributed3×
- inference3×
- language model3×
- large scale3×
- performance optimization3×
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