Staff Research Engineer (Discovery Team)

Join our Discovery Team as a Staff Research Engineer, where you will work on developing advanced AI systems that are both powerful and beneficial for humanity. You will be responsible for identifying and addressing key blockers on the path to scientific AGI, developing approaches to long-horizon task completion and complex reasoning challenges, and scaling research ideas from prototype to production. You will also create benchmarks and evaluation frameworks, implement distributed training systems, and optimize performance for large-scale model development.

• Identifying and addressing key blockers on the path to scientific AGI, working across the full stack to remove bottlenecks.
• Developing approaches to address long-horizon task completion and complex reasoning challenges essential for scientific discovery.
• Scaling research ideas from prototype to production, creating benchmarks and evaluation frameworks to measure model capabilities.
• Familiarity with performance optimization, distributed systems, vm/sandboxing/container deployment, and large scale data pipelines is highly encouraged
• Strong candidates should have familiarity with language model training, evaluation, and inference, be comfortable triaging research ideas and diagnosing problems and enjoy working collaboratively
• Can translate research concepts into scalable engineering solutions
• Thrive working collaboratively to solve problems
• Are familiar with large scale language model training, evaluation, and inference pipelines
• Have a track record of shipping ML systems that tackle challenging multi-step reasoning problems
• Enjoy obsessively iterating on immediate blockers towards longterm goals
• Show strong problem-solving skills and ability to identify technical bottlenecks in complex systems
• Have expertise in performance optimization and distributed computing systems
• 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
• Published research or practical experience in scientific AI applications or long-horizon reasoning
• Experience working with large scale data problem solving and infrastructure
• Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience
• Have 8+ years of ML research experience