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. Join us in our mission to develop advanced AI systems pushing the frontiers of science and benefiting humanity.
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 6+ 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
