About the job
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.
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
Qualifications
Minimum
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
Preferred
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