Software Engineer - Training Infrastructure

THE ROLE

As a Software Engineer 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

EXAMPLE INITIATIVES

Take a look at what we’ve built so far

• Overview of the product so far
• Training docs overview
• Story of the Training product
• Research we've done

RESPONSIBILITIES

• 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 a global training scheduler
• Design and architect reinforcement learning systems and continuous learning pipelines
• Drive long-term improvements to increase 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

REQUIREMENTS

• Bachelor’s degree or higher in Computer Science or related field
• 5+ years of experience
• Proficiency in Go, with
• Deep expertise with Kubernetes in production environments
• Advanced understanding of distributed systems concepts and performance tuning
• Proven experience designing observability systems
• Experience with ML/AI workloads and MLOps platforms

NICE TO HAVE

• Experience with distributed storage systems
• Python experience a plus
• Extensive experience with major cloud providers (AWS, GCP) and neo-cloud providers (Crusoe, DigitalOcean, Nebius)
• 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 Trainer) and distributed training techniques (FSDP, DeepSpeed).
• Experience developing AI products, tooling, or agents