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 the design and delivery of machine learning models that automatically evaluate complex technical work. Partner closely with domain specialists to turn expert judgment into structured training signals, and take models from research through to production use.
What you'll do
- Build and train models that reason over several types of technical data, including images, geometry and interaction logs.
- Translate how skilled practitioners approach problems into structured criteria a model can apply.
- Run the full learning loop: generate data, capture trajectories, analyze failures, curate datasets and evaluate continuously.
- Work side by side with subject matter experts to turn their judgment into structured labels.
- Deploy models into production pipelines that support live decisions.
Requirements
What we're looking for
- Experience building and training models from the ground up, not only applications on top of third-party models.
- Track record of shipping machine learning on multimodal data in a single pipeline.
- BS or MS in computer science, machine learning or a related quantitative field.
- Solid theoretical and practical command of neural network methods and current large-model approaches.
- Ability to turn expert judgment into well-defined machine learning problems.
- Comfort working with Python and PyTorch.
- Hands-on, autonomous working style suited to an early-stage environment, with openness to occasional extra hours when something urgent arises.
Bonus points
- Experience on the machine learning side of a CAD or generative design company.
- Machine learning experience at a chip, EDA or hardware company.
- Familiarity with reinforcement learning from human feedback, reward modeling or policy optimization.
Benefits
Compensation and benefits
- Base salary: USD 150,000 to 250,000 per year
- Equity
Location and work model
- San Francisco, CA, United States
- Hybrid, minimum 3 days per week in office
- Full-time