Resumo da vaga

Senior ML Infrastructure Engineer- Embodied AI

Requisitos e responsabilidades

Conteúdo da vaga extraído em seções para revisão mais rápida.

What You’ll Do:

  • Design, implement, and deploy scalable platforms and tools supporting machine learning training and evaluation workflows across GM.
  • Drive complex technical projects with strong ownership of implementation, code quality, and system reliability.
  • Contribute to technical design discussions and architectural decisions while collaborating with senior engineers and technical leads.
  • Work closely with partner teams to ensure platforms meet real-world ML development needs and maximize adoption.
  • Identify technical improvements and help prioritize platform investments to improve performance, reliability, and developer productivity.
  • Contribute to a strong engineering culture through high-quality code reviews, documentation, and operational excellence.
  • Support onboarding and mentoring of junior engineers and interns.

Details

  • 3+ years of experienceworking onlarge-scale distributed systems, applications, or ML infrastructure.
  • Experience designing robust services or frameworks with durable, well-designed APIs.
  • Solid understanding of machine learning workflows and hands-on experience applying ML systems in production environments.
  • Experience building reliable, high-performance, and cost-efficient systems on modern cloud infrastructure.
  • Practical experience across the ML development lifecycle, including model training, deployment, andMLOpspractices.
  • Strong cross-functional collaboration skills across teams and organizations.
  • Strong coding skills in Python or C++.
  • Interestin autonomous driving and large-scale ML systems.
  • BS, MS, or PhD in Computer Science, Mathematics, or equivalent practical experience.
  • Experience with distributed training methodologies.
  • Experience scaling ML training across large GPU/CPU clusters or specialized accelerators.
  • Familiarity with deep learning frameworks such asPyTorchor TensorFlow.
  • Experience with performance profiling and training optimization techniques and their impact on model convergence and performance.
  • Experience with advanced build systems such as Bazel, Buck, Blaze, orCMake.
  • Proficiencywith containerization and orchestration technologies (e.g., Docker, Kubernetes).

Nice to Have:

  • The salary range for this role is $153,200.00 to $234,100.00. The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position.
  • Bonus Potential: An incentive pay program offers payouts based on company performance, job level, and individual performance.

Benefits:

  • Benefits: GM offers a variety of health and wellbeing benefit programs. Benefit options include medical, dental, vision, Health Savings Account, Flexible Spending Accounts, retirement savings plan, sickness and accident benefits, life insurance, paid vacation & holidays, tuition assistance programs, employee assistance program, GM vehicle discounts and more.
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FocoML Infrastructure EngineeringÁrea da vaga
Sinal de senioridadeSeniorNível do candidato
StackDocker, Kubernetes, PythonSkills principais
Localização1 país aceitoElegibilidade

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