About the company Our client is a fast-growing software company. The role Raydar is recruiting for this role on behalf of our client. Join as an early engineer working across backend services and applied machine learning. The role involves building data processing and retrieval…
About the company
Our client is a fast-growing software company.
The role
Raydar is recruiting for this role on behalf of our client. Join as an early engineer working across backend services and applied machine learning. The role involves building data processing and retrieval systems and taking models into secure production environments, with broad ownership of how the pieces fit together.
What you'll do
- Build backend services and data pipelines that handle large volumes of video, audio and text.
- Develop retrieval pipelines that surface relevant information from large datasets.
- Integrate large language models and vision-language models into production applications.
- Deploy secure, distributed systems and machine learning inference pipelines.
- Take projects from initial architecture through to a live release, owning each stage.
- Explain system design choices and trade-offs clearly to colleagues and stakeholders.
Requirements
What we're looking for
- 3+ years of software engineering experience, ideally 2+ years in applied machine learning.
- Strong Python backend skills and familiarity with machine learning operations.
- Production experience deploying language or vision-language models, or custom inference pipelines.
- Solid working knowledge of scaling backend infrastructure.
- Strong technical communication skills.
- Comfort working in fast-paced, high-impact startup settings.
- Willingness to undergo a government background check.
Benefits
Compensation and benefits
- Base salary: USD 165,000 to 250,000 per year
- Equity
- Health and dental coverage
- Retirement plan with employer contribution
- Unlimited paid time off
Location and work model
- San Francisco, CA, United States or New York City, NY, United States
- On-site
- Full-time