Machine Learning Infrastructure Engineer

About the company

Our client is a fast-growing software company.

The role

Raydar is recruiting for this role on behalf of our client. Own and scale the systems that train and run machine learning models in production, partnering with researchers so new models ship quickly and dependably. This is an individual contributor role for a versatile engineer who understands how models behave under load and can improve speed, stability and spend.

What you'll do

- Run and enhance the production systems that deliver model predictions, with a focus on latency, observability and uptime.

- Set up and refine training environments for models across a broad range of sizes.

- Develop logging, metrics and alerting across the machine learning stack.

- Create internal data pipelines and tools that shorten the path from research experiment to production release.

- Design infrastructure that distributes workloads across several cloud vendors, balancing quality, response time and cost.

Requirements

What we're looking for

- 3+ years of experience in machine learning infrastructure.

- Proven track record operating production model deployment systems in a demanding, high-performing engineering team.

- Strong Python skills and systems engineering experience with Kubernetes.

- Deep expertise in diagnosing GPU performance limits, tuning model deployment and monitoring.

- Comfort working in a fast-paced startup environment.

Bonus points

- Hands-on experience with training and deployment on a small number of machines.

- Prior work at a startup that develops its own models.

- Technical degree from a leading program.

- Research publications at top machine learning venues.

- Open source contributions in machine learning tooling.

- Strong results in math or computer science competitions.

Benefits

Compensation and benefits

- Base salary: USD 200,000 to 300,000 per year

- Equity

- Health, dental and vision coverage

- Parental leave

- Flexible time off

Location and work model

- San Francisco, California, United States

- On-site, 5 days per week in office

- Full-time