Staff/Senior Machine Learning Research Engineer, Intelligent Systems
$250k - $350k • Remote • San Francisco, CA; New York, NY
Posted 5d ago
Job Location
San Francisco, CA; New York, NY
Tech Stack
Remote Work Policy
Fully remote
Categories
Machine Learning Engineer
About the job
Scale is seeking a Staff Machine Learning Research Engineer to join the Applied Intelligence Systems (AIS) team, which is part of the Scale Generative AI Platform. This role focuses on advancing agentic AI applications for enterprise and government use cases. You will work across the full spectrum of technical challenges within AIS, contributing to areas such as model training and fine-tuning, designing evaluation systems, building improvement loops from production data, prototyping novel agent architectures, and developing internal tooling. This is a hands-on research and engineering position where you will write code, ship production systems, and set the AI/ML technical direction for the team, influencing methods, architectures, and standards for others.
Responsibilities
- Move across core problem areas like training/fine-tuning, inference, retrieval, evaluation, observability, orchestration, tool-use infrastructure, and applied research on new agent capabilities, focusing on areas with the highest technical leverage.
- Research and prototype novel methods for improving agent performance in production settings, such as continuous learning loops, automated data generation, and online/offline RL, validating them with rigorous experiments.
- Build AI agents and internal tooling to streamline AIS processes, reducing time spent on repetitive tasks and allowing teams to focus on complex problems.
- Collaborate with ML engineers, software engineers, product managers, customers, data annotators, and Forward Deployed Engineers to take work from concept to production.
- Set AI/ML technical direction, mentor engineers and scientists, and improve experimental rigor across the organization.
- Write code for training pipelines, evaluation systems, and infrastructure, and ship production systems.
Requirements
- 5+ years of experience as an ML engineer or applied/research scientist, with direct experience training or fine-tuning models in production systems.
- PhD in Computer Science, Electrical Engineering, or a related field.
- Broad, hands-on fluency across the agentic ML stack, including model training/fine-tuning (SFT, RLHF/RLAIF, reward modeling), evaluation/observability infrastructure, and agent architecture (tool use, planning, memory, multi-agent orchestration).
- Demonstrated depth or expertise in at least one area within the AI/ML domain.
- Ability to work across different problem areas rather than specializing, quickly learning unfamiliar system components.
- Track record of partnering with software engineers to productionize research and experimental work, including shipping code to production.
- Proven ability to set AI/ML technical direction, choose methods and architectures adopted by other teams, and collaborate across functions to bring solutions to production.
- Track record of mentoring engineers and scientists, providing and receiving substantive technical feedback at a staff level, and influencing decisions and standards beyond your own team.