Staff/Senior Machine Learning Research Engineer (AI)

Salary: $250,000–$350,000 USD per year, plus potential equity and benefits.

Company

Scale AI develops reliable AI systems, high-quality training data, and full-stack technologies for generative AI, defense, autonomous vehicles, enterprise, and government applications.

What you will do

• Move across training and fine-tuning, inference, memory and retrieval, evaluation and observability, orchestration, tool use, and applied agent research.
• Research and prototype methods for improving agent performance, including continuous learning, automated data generation, curriculum generation, and online or offline reinforcement learning.
• Build AI agents, training pipelines, evaluation systems, infrastructure, and internal tooling that improve experimentation and production workflows.
• Partner with ML and software engineers, product managers, customers, data annotators, and Forward Deployed Engineers to translate enterprise and government requirements into production ML capabilities.
• Set AI/ML technical direction, mentor engineers and scientists, and establish rigorous experimental and engineering standards across teams.

Requirements

• 5+ years of experience as an ML engineer or applied/research scientist, including training or fine-tuning models in production.
• PhD in Computer Science, Electrical Engineering, or a related field.
• Hands-on experience across model training and fine-tuning, SFT, RLHF/RLAIF, reward modeling, evaluation and observability infrastructure, and agent architectures.
• Ability to move across unfamiliar ML problem areas and productionize research in partnership with software engineers.
• Experience setting technical direction, influencing cross-functional decisions, mentoring engineers and scientists, and delivering code to production.

Nice to have

• Published research, open-source contributions, or patents in agent training, LLM alignment, or applied ML.
• Experience with online learning, continuous fine-tuning, automated data generation, or curriculum generation from production traces.
• Experience optimizing training efficiency, latency, cost, or inference efficiency at scale.
• Experience in regulated, enterprise, or government environments.
• Experience taking novel training methods or agent architectures from prototype to reliable production systems.

Culture & Benefits

• Health, dental, and vision coverage.
• Retirement benefits, learning and development stipend, and generous paid time off.
• Potential commuter stipend and equity compensation for eligible roles.
• Inclusive equal opportunity workplace with reasonable accommodations available during the application and recruiting process.