About the company
Our client is a fast-growing software company.
The role
Raydar is recruiting for this role on behalf of our client. Drive applied machine learning research that turns new ideas into deployed models, spanning predictive modeling, optimization and language-model techniques. The work also involves assembling the datasets those models depend on. You will join a compact, hands-on research group where individual work ships quickly.
What you'll do
- Lead research projects from early idea through validation and deployment into production.
- Develop predictive and optimization models for complex settings where goals shift over time.
- Assemble and curate specialized datasets that carry useful signal about real-world systems.
- Pair language-model capabilities with custom quantitative models to improve accuracy and flexibility.
- Refresh and retrain models as outside conditions evolve.
Requirements
What we're looking for
- Doctoral degree in a quantitative discipline such as computer science, machine learning or statistics.
- A track record of taking research projects all the way to live production models.
- Hands-on background in forecasting of sequential data, mathematical modeling or optimization methods.
- Strong coding skill in Python and experience with a mainstream neural network library.
Bonus points
- Grounding in the theory behind machine learning or theoretical computer science.
- Substantial experience with large language models, including design, tooling and evaluation.
- Experience with machine learning at scale, such as building pipelines, running large experiments, fitting parameters and data engineering.
Benefits
Compensation and benefits
- Base salary: USD 200,000 to 300,000 per year
- Equity
- Relocation support
Location and work model
- San Francisco, California, United States
- On-site, 5 days per week in office
- Full-time
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