Software Engineer - Training Infrastructure
Architects and leads development of scalable ML training infrastructure, including scheduling, storage, networking, and reinforcement learning systems. Requires proficiency in Go, Kubernetes expertise, distributed systems knowledge, and experience with cloud providers and ML workloads.
About the job
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 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
Requirements
- Bachelor’s degree or higher in Computer Science or related field
- Proficiency in Go, with Python experience a plus
- Deep expertise with Kubernetes in production environments
- 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
- Proven experience designing observability systems
- Experience with ML/AI workloads and MLOps platforms highly valued
Nice to Have
- Experience with distributed storage systems
- 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
Benefits
- Competitive compensation, including meaningful equity
- 100% coverage of medical, dental, and vision insurance for employee and dependents
- Generous PTO policy including company wide Winter Break
- Paid parental leave
- Company-facilitated 401(k)
- Exposure to a variety of ML startups
Skills
Go, Python, Kubernetes, AWS, GCP, Distributed Systems, Observability, PyTorch, MLOps, Temporal, Airflow, Deepspeed, Fsdp
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