About the company Our client is a cybersecurity company. The role Raydar is recruiting for this role on behalf of our client. Own the design, training and production deployment of machine learning systems behind a core detection capability. Partner closely with technical…
About the company
Our client is a cybersecurity company.
The role
Raydar is recruiting for this role on behalf of our client. Own the design, training and production deployment of machine learning systems behind a core detection capability. Partner closely with technical leadership and help set engineering standards on a small, fast-moving team.
What you'll do
- Design and build machine learning models that flag suspicious activity as it happens.
- Take models from research into production and measure how they behave at scale in a live environment.
- Architect systems that can absorb significant growth in data volume and traffic.
- Talk with customers to understand the threats they face and uncover new signals that strengthen detection.
- Set technical patterns, push for correctness and raise the quality bar across the team.
- Own systems end to end, from data pipelines through serving and infrastructure.
Requirements
What we're looking for
- 4+ years of machine learning engineering experience, including shipping models to production at scale.
- A solid mathematical grounding, including statistics, linear algebra and applied modeling of anomalous behavior.
- Experience training and deploying models, with good judgment about which approach fits a problem and where its limits lie.
- Strong engineering fundamentals, including clean production-grade Python and end-to-end system ownership.
- Hands-on background with low-latency data processing and scalable storage and compute systems.
- High agency: you scope your own work, propose solutions and execute without direction.
- Fast learner who ships quickly and is comfortable in a small, high-autonomy team.
- Pride in craft and in building things that hold up over time.
Bonus points
- Background in high-stakes domains such as quantitative finance or fraud detection.
- Experience shipping production machine learning at an early-stage company or on an ML infrastructure team.
- Degree in computer science, math or a quantitative field.
- First-author publication at a leading machine learning conference.
Benefits
Compensation and benefits
- Base salary: USD 250,000 to 300,000 per year
- Equity
- Health, dental and vision coverage
- Retirement plan
- Commission
Location and work model
- San Francisco, CA, United States
- On-site, 5 days per week in office
- Full-time