Raydar→
Machine Learning Engineer at Raydar in San Francisco, CA
Skills
Job Description
Machine Learning 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. Act as the first engineering hire, blending general software development with applied machine learning to produce dependable, data-driven product capabilities. The work involves close customer contact, independent technical and product judgment, and laying the groundwork for a future engineering organization.
What you'll do
- Create bespoke machine learning models for categorizing inputs, forecasting potential and setting priorities.
- Build ranking, scoring and quality-assessment components that cope with unpredictable model outputs.
- Develop measurement frameworks that link product signals to business impact.
- Set up data collection and processing pipelines that convert raw inputs into useful insight.
- Shift fluidly among modeling, analysis, coding and product choices on your own initiative.
- Take charge of core AI workflows, making them resilient through job queues, retry logic, monitoring, testing and orchestration.
Requirements
What we're looking for
- At least 3 years of experience delivering production software, with broad full-stack skill and the autonomy to work on any layer.
- Hands-on background in applied machine learning or data science, for example with large language models, retrieval, ranking or experimentation.
- Solid Python and SQL skills, plus sound reasoning about how models behave, where they fail and how to judge quality with imperfect data.
- A self-directed, ownership-driven approach and comfort turning unclear problems into working product.
- Daily, expert use of AI coding assistants, with good sense about when they help and when they do not.
- Prior founder experience or time as an early engineer at a young startup.
Bonus points
- Track record of taking models from exploratory analysis to live customer-facing systems.
- Experience gathering signals from messy, real-world data sources.
- Exposure to causal inference, forecasting or statistical measurement.
- Familiarity with search, recommendation or analytics products.
- Ability to build user-facing features, APIs or internal tooling when required.
Benefits
Compensation and benefits
- Base salary: USD 130,000 to 250,000 per year
- Equity
- Health insurance
- Retirement plan
- Flexible time off
Location and work model
- San Francisco, CA, United States
- On-site
- Full-time