Research Engineer, Discovery

About the Team

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.

About the Role

As a Research Engineer on our team you will work end to end across the whole model stack, identifying and addressing key infra blockers on the path to scientific AGI. Strong candidates should have familiarity with elements of language model training, evaluation, and inference and eagerness to quickly dive and get up to speed in areas they are not yet an expert on. This may include performance optimization, distributed systems, VM/sandboxing/container deployment, and large scale data pipelines.

Responsibilities

• Design and implement large-scale infrastructure systems to support AI scientist training, evaluation, and deployment across distributed environments
• Identify and resolve infrastructure bottlenecks impeding progress toward scientific capabilities
• Develop robust and reliable evaluation frameworks for measuring progress towards scientific AGI
• Build scalable and performant VM/sandboxing/container architectures to safely execute long-horizon AI tasks and scientific workflows
• Collaborate to translate experimental requirements into production-ready infrastructure
• Develop large scale data pipelines to handle advanced language model training requirements
• Optimize large scale training and inference pipelines for stable and efficient reinforcement learning

Qualifications

Minimum Qualifications

• 6+ years of highly-relevant experience in infrastructure engineering with demonstrated expertise in large-scale distributed systems
• Strong communication skills and collaborative approach
• Deep knowledge of performance optimization techniques and system architectures for high-throughput ML workloads
• Experience with containerization technologies (Docker, Kubernetes) and orchestration at scale
• Proven track record of building large-scale data pipelines and distributed storage systems
• Ability to diagnose and resolve complex infrastructure challenges in production environments
• Ability to work effectively across the full ML stack from data pipelines to performance optimization
• Experience collaborating with researchers to scale experimental ideas
• Ability to iterate rapidly from experimentation to production
• Bachelor’s degree or an equivalent combination of education, training, and/or experience

Preferred Qualifications

• Experience with language model training infrastructure and distributed ML frameworks (PyTorch, JAX, etc.)
• Background in building infrastructure for AI research labs or large-scale ML organizations
• Knowledge of GPU/TPU architectures and language model inference optimization
• Experience with cloud platforms (AWS, GCP) at enterprise scale
• Familiarity with VM and container orchestration
• Experience with workflow orchestration tools and experiment management systems
• History working with large scale reinforcement learning
• Familiarity with large scale data pipelines (Beam, Spark, Dask, etc.)