Назад
2 дня назад

Staff Software Engineer, Simulation ML Infrastructure (AI/ML)

251 000 - 310 000$
Формат работы
onsite
Тип работы
fulltime
Грейд
senior
Английский
c1
Страна
US
Вакансия из списка Hirify.GlobalВакансия из Hirify Global, списка международных tech-компаний
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Описание вакансии

Текст:
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TL;DR
Staff Software Engineer, Simulation ML Infrastructure (AI/ML): Building and scaling distributed infrastructure for foundation-model-powered autonomous-driving simulations with an accent on massive model scaling, ML accelerator optimization, and planet-scale data generation. Focus on designing ML lifecycle systems, profiling accelerator performance, and solving complex infrastructure challenges across data engineering, model training, and simulation.

Location: Onsite in Mountain View or San Francisco, California, United States

Salary: $251,000–$310,000 USD gross annual base salary, plus discretionary annual bonus, equity incentives, and company benefits.

Company

Waymo develops autonomous driving technology and operates a fully autonomous ride-hailing service powered by the Waymo Driver.

What you will do

  • Build advanced AI/ML infrastructure for realistic, multi-agent autonomous-driving simulations using large foundation models.
  • Design and scale distributed systems supporting planet-scale dataset generation, model training, and evaluation across the ML lifecycle.
  • Drive architecture and technical decisions for large, complex systems spanning data engineering, model development, and simulation.
  • Collaborate with the Realism Modeling teams in London and Oxford to improve simulation fidelity and steerability.
  • Translate product and business objectives into measurable technical requirements and aligned system designs.

Requirements

  • 6+ years of professional software engineering experience, including at least 4 years building or scaling large-scale ML infrastructure.
  • Experience developing, training, deploying, and optimizing machine learning systems from data through production models.
  • Strong experience with ML infrastructure tools such as DeepSpeed, PyTorch, TensorFlow, Ray, or similar frameworks.
  • Knowledge of modern ML models and algorithms, including autoregressive transformers, and experience profiling ML accelerators to identify bottlenecks.
  • Ability to lead ambiguous technical problems end to end, build infrastructure and tooling, and communicate complex concepts clearly.

Nice to have

  • Practical familiarity with autonomous driving, simulation, and ML accelerators.

Culture & Benefits

  • Work with a research engineering team advancing high-fidelity autonomous-driving simulations.
  • Collaborate across international teams in the United States and United Kingdom.
  • Eligibility for a discretionary annual bonus and equity incentive plan.
  • Generous company benefits program, subject to eligibility requirements.

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