About the company Our client is a technology company. The role Raydar is recruiting for this role on behalf of our client. Own and build the backend and machine learning infrastructure that supports a fleet of deployed hardware. The work spans data pipelines, compute resources…
About the company
Our client is a technology company.
The role
Raydar is recruiting for this role on behalf of our client. Own and build the backend and machine learning infrastructure that supports a fleet of deployed hardware. The work spans data pipelines, compute resources and internal tooling, shifting to wherever the current engineering bottleneck sits while helping establish early engineering standards.
What you'll do
- Build and own the platform that runs large model training jobs, covering job scheduling, resilience to failures and network connectivity.
- Design data pipelines that move very large volumes of telemetry and video into training while keeping compute fully utilized.
- Architect an automated feedback loop that returns production data from deployed devices back into model improvement.
- Shift focus week to week toward the most pressing bottleneck, whether a deployment path, a shared library or a monitoring gap.
- Develop internal tooling that helps every engineer and researcher work faster.
Requirements
What we're looking for
- 2+ years of experience in distributed systems or machine learning infrastructure.
- Background at a high-caliber technology company or lab with a demanding hiring bar.
- Experience shipping production software on physical robots or autonomous vehicles.
- Solid engineering fundamentals in backend systems, data movement and compute infrastructure.
- Hands-on work with very large datasets or training jobs spread across many machines.
- Ability to work on-site 6 days per week.
Benefits
Compensation and benefits
- Base salary: USD 200,000 to 350,000 per year
- Equity
- Health, dental and vision coverage
Location and work model
- San Francisco, CA, United States
- On-site, 6 days per week in office
- Full-time