Staff ML Research Engineer — Production Agentic AI

Scale AI; San Francisco (CA); On-site; USD 250,000 - 350,000; Full time

Benefits offered by this job

Health insurance

Dental and Vision benefits

Retirement benefits

Learning stipend

Generous PTO

Commuter stipend

Job summary

Scale AI is hiring a Staff Machine Learning Research Engineer to work across AIS, training models, developing evaluation loops, and building production-ready systems. You will guide AI/ML direction across teams and mentor engineers and scientists, applying novel methods to enterprise-scale problems.

The role emphasizes cross-team collaboration, rigorous experimentation, and shipping production-grade AI capabilities in a fast-moving environment.

Qualifications

• 5+ years of experience in ML engineering or applied research with production training/fine-tuning.
• PhD in CS/EE or related field.
• Broad fluency across ML stack: training, evaluation, architectures, and tooling.
• Ability to move across problem areas and ship production code.
• Experience partnering with software engineers to productionize research.
• Mentoring engineers and scientists at staff level.

Responsibilities

• Move across AIS’s core ML problem areas as needed, training/fine-tuning and inference.
• Prototype novel methods for agent performance in production settings with rigorous experiments.
• Build internal tooling to reduce bottlenecks and speed up experiments.
• Collaborate with engineers, product managers, customers, and FD Engineers to deploy solutions.
• Set AI/ML direction and raise the bar for rigor across AIS.

Education

PhD in Computer Science or Electrical Engineering

Job description

Scale AI is hiring a Staff Machine Learning Research Engineer to work across AIS, training models, developing evaluation loops, and building production-ready systems. You will guide AI/ML direction across teams and mentor engineers and scientists, applying novel methods to enterprise-scale problems.

The role emphasizes cross-team collaboration, rigorous experimentation, and shipping production-grade AI capabilities in a fast-moving environment.