Staff Research Engineer, Discovery Team
Research Engineer works end-to-end to remove bottlenecks toward scientific AGI, focusing on long-horizon reasoning, computer use, and model capabilities. Requires 8+ years ML experience, expertise in language model pipelines, distributed systems, and collaborative problem-solving.
About the job
About the Team
Our team is focused on building an AI scientist capable of solving long-term reasoning challenges and pushing the scientific frontier. We're currently improving models' abilities to use computers as a laboratory for long-horizon tasks and scientific workflows.
About the Role
As a Research Engineer, you will work end-to-end to identify and address key blockers on the path to scientific AGI. Strong candidates have familiarity with language model training, evaluation, and inference, and enjoy collaborative problem-solving.
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
You may be a good fit if you
- 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 long-term 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
Strong candidates may also have
- 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)
- 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
Logistics
Education requirements: 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. Some roles may require more time in our offices.
Skills
PyTorch, Transformers, Distributed Training, Kubernetes, Docker, Performance Optimization, Reinforcement Learning, Language Models, Inference Optimization, Data Pipelines
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