Remote ML Engineer: Build Benchmarks & Production Pipelines

raydar; Northern (KY); Hybrid; USD 170,000 - 270,000; Full time

Benefits offered by this job

Competitive equity

Job summary

raydar is a fast-growing AI data infrastructure company seeking a Forward Deployed Machine Learning Engineer to join our Benchmarks and Evaluations vertical. You will collaborate with the general manager, researchers, and enterprise customers to establish the foundation for evaluating foundation models across domains and modalities.

The role blends hands-on ML evaluation, backend infrastructure, and customer-facing delivery in a high-ownership environment with remote US work flexibility.

Qualifications

• 4+ years of engineering experience with ML model evaluation.
• Experience deploying ML evaluation systems to production.
• Experience with backend data pipelines, storage, and orchestration.
• Strong written communication and stakeholder management.
• U.S. work authorization.

Responsibilities

• Define, design, and build production benchmarks and evaluations with customers and internal researchers.
• Build and own backend infrastructure, including data pipelines, execution environments, storage, and orchestration.
• Create sandboxed environments for agentic evaluations involving tools, code execution, and multi-step tasks.
• Own the engineering portion of customer engagements from technical scoping through production delivery.
• Identify repeatable evaluation patterns and infrastructure gaps that can become scalable products.
• Move quickly through ambiguity while maintaining strong technical judgment and clear written communication.

Skills

ML evaluation

Benchmark design

Backend infra

Data pipelines

Customer-facing

Production deployment

Education

Bachelor's degree in CS/Physics

Job description

raydar is a fast-growing AI data infrastructure company seeking a Forward Deployed Machine Learning Engineer to join our Benchmarks and Evaluations vertical. You will collaborate with the general manager, researchers, and enterprise customers to establish the foundation for evaluating foundation models across domains and modalities.

The role blends hands-on ML evaluation, backend infrastructure, and customer-facing delivery in a high-ownership environment with remote US work flexibility.