Machine Learning Engineer
Raydar
Machine Learning Engineer
Raydar
AI-powered recruiting agency for tech roles
Raydar · San Francisco, United States (Hybrid) · Full-time · Posted 2026-11-12
Salary: USD 165,000–250,000
Workplace: hybrid
About the company
Our client is a government technology company.
The role
Raydar is recruiting for this role on behalf of our client. Build machine learning and backend systems that handle large multimedia datasets, owning work from first design through production release in secure, distributed environments. The role suits an engineer who wants to see projects through from concept to launch.
What you'll do
- Plan how large multimedia datasets move through retrieval pipelines.
- Integrate large language models and vision-language models into production applications.
- Build and deploy distributed backend services and machine learning inference pipelines.
- Take projects from initial concept to production release.
- Explain system designs and trade-offs clearly to technical and non-technical colleagues.
What we're looking for
- At least 3 years of experience in backend engineering, with strong Python skills.
- Familiarity with ML operations and common frameworks for web APIs, LLM applications and deep learning.
- Production experience deploying large language models, vision-language models or custom ML inference pipelines.
- Solid knowledge of designing and scaling backend infrastructure.
- Clear technical communication, with the ability to explain systems and trade-offs.
- U.S. work authorization and willingness to undergo an FBI background check.
Compensation and benefits
- Base salary: USD 165,000 to 250,000 per year
- Retirement plan with 3% employer contribution
- Health and dental coverage
- Flexible time off
Location and work model
- New York City, NY, United States (preferred) or San Francisco, CA, United States
- Hybrid, in office at least 3 days a week
- Full-time
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Raydar is an AI-powered talent acquisition firm that serves tech-focused organizations on a contingency basis, charging client companies only after a successful hire. It uses proprietary AI to identify a large pool of candidates and conducts a short 15-minute AI-driven interview (recorded and transcribed) before human recruiters review materials, provide interview prep, and assist with offers and negotiations. What sets it apart is the blend of automated sourcing and screening with hands-on human oversight and tailored client support. Its goal is to help organizations hire more efficiently by speeding up sourcing while maintaining thorough evaluation and negotiation assistance.
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