- Raydar
- San Francisco, CA
- Full-Time
- 35 days ago
- $200,000–$300,000 / year
Machine Learning Infrastructure Engineer.
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Machine Learning Infrastructure Engineer: our view in 3 lines...
- The Role:This individual-contributor engineering role supports machine learning research by building and operating infrastructure for training and running models in production.
- The Person:The person will operate production prediction systems, refine model training environments, build logging, metrics and alerting, create internal data pipelines and tools, and design infrastructure across cloud vendors.
- Requirements:The role requires 3+ years of machine learning infrastructure experience, strong Python skills, Kubernetes systems engineering experience, and expertise diagnosing GPU performance limits, tuning model deployment, and monitoring.
About the role
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