Role overview

Software Engineer- Training Infrastructure

Requirements and responsibilities

Readable role content extracted into sections for faster review.

Details

  • Overview of the product so far
  • Training docs overview
  • Story of the Training product
  • Research we've done
  • 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
  • Bachelor’s degree or high 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
  • 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 Trainier) and distributed training techniques (FSDP, DeepSpeed).
  • Experience developing AI products, tooling, or agents
  • Competitive compensation, including meaningful equity.
  • 100% coverage of medical, dental, and vision insurance for employee and dependents
  • Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!)
  • Paid parental leave
  • Fertility and family-building stipend through Carrot
  • Company-facilitated 401(k)
  • Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities.
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Browse stack
FocusTraining PlatformRole area
Seniority signalOpen levelCandidate level
StackAWS, GCP, KubernetesPrimary skills
Location1 accepted countryEligibility

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