As a Staff Infrastructure Engineer on our team you will work end to end, identifying and addressing key infra blockers on the path to scientific AGI. 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.
Join us in our mission to develop advanced AI systems that are both powerful and beneficial for humanity.
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
You may be a good fit if you:
Have 3+ years of highly relevant experience in infrastructure engineering with demonstrated expertise in large-scale distributed systems
Are a strong communicator and enjoy working collaboratively
Possess deep knowledge of performance optimization techniques and system architectures for high-throughput ML workloads
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
Excel at diagnosing and resolving complex infrastructure challenges in production environments
Can work effectively across the full ML stack from data pipelines to performance optimization
Have experience collaborating with other researchers to scale experimental ideas
Strong candidates may also have:
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
Comfort with large scale data pipelines (Beam, Spark, Dask, …)
