Machine Learning Researcher, RL & Agentic Systems
Protege - Remote, United States
Posted May 28, 2026
Benefits
- Parental leave
- Not verified
- Non-birth-parent leave
- Not verified
- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
- Not verified
- Relocation assistance
- Not verified
- Childcare support
- Not verified
- Learning budget
- Not verified
- Verification
- Not verified
- Salary
- Not verified
- 401(k) match
- Not verified
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Market context
- U.S. role benchmark (BLS OEWS)
- $111,944 U.S. median for this role
- Projected growth (BLS Employment Projections)
- +13.7% - Much faster than average
Matched to SOC 15-1252 - Data and ML aggregate by role bucket.
Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.
Role
Schedule
- Shift type
- Not verified
- Weekend work
- Not verified
Application
- Cover letter
- Not verified
- Assessment
- Not verified
- Deadline
- Not stated
Where they hire
State eligibility is not yet verified.
About this role
Machine Learning Researcher, RL & Agentic Systems Remote, United States Company Overview: We are building Protege to solve the biggest unmet need in AI - getting access to the right training data. The process today is time intensive, incredibly expensive, and often ends in failure. The Protege platform facilitates the secure, efficient, and privacy-centric exchange of AI training data. Solving AI's data problem is a generational opportunity. We're backed by world-class investors and already powering partnerships with some of the most ambitious teams in AI. The company that succeeds will be one of the largest in AI - and in tech. We're a lean, fast-moving, high-trust team of builders who are obsessed with velocity and impact. Our culture is built for people who thrive on ambiguity, own outcomes, and want to shape the future of data and AI. About DataLab DataLab exists because truly useful data is rare - and the frontier of AI development only moves forward when high-quality data makes it possible. We believe data is one of the most underdeveloped layers of the AI stack. Our work focuses on building and evaluating high-value datasets grounded in real-world workflows and economically meaningful tasks. We work across multiple domains to create safe, high-fidelity datasets that preserve the structure and context needed to train advanced AI systems. Our research spans data quality, evaluation design, privacy-preserving transformation, workflow reconstruction, and task-grounded AI training data. At DataLab, applied research is tightly connected to real-world deployment. Researchers work directly with large-scale datasets, production
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