Staff Machine Learning Engineer, Offline Infrastructure

Unity Technologies

Mountain View, CA, United States
Base: $209,700 - $283,800 usd; bonus/equity: emplo...
On-site
Strong experience building large-scale ml pipelines
Experience with distributed computing frameworks ray spark flink
Deep experience designing production-grade data pipelines
Unity Technologies is seeking a Staff Machine Learning Engineer to design and enhance their large-scale offline ML platform, focusing on data pipelines and distributed training workflows. The role requires strong technical expertise in building ML infrastructure and collaborating with various teams to ensure scalability and reliability

Job Summary

  • Unity Vector builds an offline ML platform that powers insight, experimentation, attribution, and AI-driven decision-making across the company.
  • The role focuses on building reliable infrastructure for generating training datasets, orchestrating ML workflows, and enabling efficient distributed model training at scale.
  • Benefits include comprehensive health insurance, employee stock ownership, competitive retirement plans, and generous vacation and personal days.

Matching Summary

Match Score: 85

Unity Technologies is seeking a Staff Machine Learning Engineer to design and enhance their large-scale offline ML platform, focusing on data pipelines and distributed training workflows. The role requires strong technical expertise in building ML infrastructure and collaborating with various teams to ensure scalability and reliability.

Salary

Base: $209,700 - $283,800 USD; Bonus/Equity: Employee stock ownership mentioned; Benefits: Comprehensive health, life, disability insurance, commute subsidy, retirement plans

Skills & Requirements

Must-have

  • Strong experience building large-scale ML pipelines
  • Experience with distributed computing frameworks Ray Spark Flink
  • Deep experience designing production-grade data pipelines
  • Strong programming skills in Python for distributed workloads
  • Experience with modern data infrastructure lakes warehouses

Nice-to-have

  • Ability to lead technical direction without formal authority
  • Familiarity with Ray Data and Ray Train ecosystems
  • Systems thinking for performance and cost tradeoffs
  • Experience with streaming platforms and batch data

Key Requirements

  • Proven ability to lead technical direction and influence architectural decisions
  • Strong systems thinking with reasoning about distributed system tradeoffs

Work Rights

Not specified

Tailored Resume

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