Research Engineer (Discovery)

Join our team as a Research Engineer, where you will play a crucial role in advancing scientific AGI. You will design and implement large-scale infrastructure systems, identify and resolve infrastructure bottlenecks, and develop robust evaluation frameworks. You will also build scalable VM/sandboxing/container architectures, collaborate on translating experimental requirements into production-ready infrastructure, and optimize training and inference pipelines. This position offers comprehensive benefits, a competitive salary, and the opportunity to work on cutting-edge AI research.

• Identifying and addressing key infrastructure blockers on the path to scientific AGI.
• Designing and implementing large-scale infrastructure systems to support AI scientist training, evaluation, and deployment.
• Developing robust and reliable evaluation frameworks for measuring progress towards scientific AGI.
• Have 6+ years of highly relevant experience in infrastructure engineering with demonstrated expertise in large-scale distributed systems
• Strong candidates should have familiarity with performance optimization, distributed systems, vm/sandboxing/container deployment, and large scale data pipelines
• Familiarity with language model training, evaluation, and inference is highly encouraged
• Possess deep knowledge of performance optimization techniques and system architectures for high-throughput ML workloads
• Have experience collaborating with other researchers to scale experimental ideas
• Excel at diagnosing and resolving complex infrastructure challenges in production environments
• Are a strong communicator and enjoy working collaboratively
• Can work effectively across the full ML stack from data pipelines to performance optimization
• Have experience with containerization technologies (Docker, Kubernetes) and orchestration at scale
• Have proven track record of building large-scale data pipelines and distributed storage systems
• 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
• Experience with cloud platforms (AWS, GCP) at enterprise scale
• Knowledge of GPU/TPU architectures and language model inference optimization
• Familiarity with VM and container orchestration
• Experience with workflow orchestration tools and experiment management systems
• History working with large scale reinforcement learning
• Comfort with large scale data pipelines (Beam, Spark, Dask, …)
• Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience