Software Engineer - Training Infrastructure

As a Software Engineer at on the Training Infrastructure team, you'll architect and lead development of our training platform, supporting top tier research engineers and model developers. You'll make key technical decisions for the infrastructure enabling developers to deploy, scale, and monitor their workloads with high performance and reliability.

You'll own scheduling, storage, networking, reliability, and observability of technical systems in the training stack

- Design and architect scalable infrastructure systems for our ML training platform (e.g. scheduling, storage, and networking)

- Partner closely with developers and research engineers to translate complex training requirements into technical solutions

- Design and architect reinforcement learning systems and continuous learning pipelines

- Drive long-term improvements to improve reliability of systems and velocity of development

- Partner closely with SRE and Capacity teams to unlock state of the art training infrastructure

- Make critical architectural decisions balancing performance with system reliability

- Lead technical discussions and mentor junior engineers on infrastructure best practices

- Contribute to long-term technical strategy and infrastructure roadmap

- Bachelor's degree or high in Computer Science or related field

- Extensive experience with major cloud providers (AWS, GCP) and neo-cloud providers (Crusoe, DigitalOcean, Nebius) a plus

- Advanced understanding of distributed systems concepts and performance tuning

- Experience with ML/AI workloads and MLOps platforms highly valued

- Experience with workload orchestration platforms like Temporal or Airflow

- Familiarity or experience with the open source training stack and frameworks (NCCL, PyTorch, Megatron, NemoRL, VeRL, Axolotl, HF Trainier) and distributed training techniques (FSDP, DeepSpeed).