AI Research Scientist - Fully Remote | Upto $90/hr

mercor; United States; Remote; USD 83,000 - 124,000; Full time

Job summary

Mercor seeks a Machine Learning Engineer for model evaluation and experimentation. This remote, hourly contractor role focuses on turning research ideas into well-defined tasks, running end-to-end experiments, and evaluating frontier models with researchers to ensure rigor and fairness.

The ideal candidate has MSc/PhD in ML/CS, 1+ year research experience, and strong Python/Git skills, with a solid understanding of large language models and their evaluation.

Qualifications

• MSc or PhD in machine learning, computer science, or a related STEM field.
• 1+ years in a research or research-engineering role.
• Experience in training and evaluating ML models and conducting end-to-end experiments.
• Proficiency in Python and Git.
• Strong understanding of large language models and their evaluation.

Responsibilities

• Design tasks by transforming real ML research ideas into well-defined, multi-step tasks.
• Run experiments by implementing changes, executing training experiments, and analyzing results to define correct solutions.
• Explore reinforcement learning concepts such as reward functions and training behavior in task development.
• Evaluate frontier models' performance on tasks and identify areas of improvement.
• Collaborate with researchers to ensure tasks are consistent, rigorous, and fair.

Skills

Python programming

Research experience

End-to-end experiments

Education

MSc or PhD in ML/CS

Tools

Git

Job description

Mercor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark, General Catalyst, Peter Thiel, Adam D'Angelo, Larry Summers, and Jack Dorsey.

Job Details

• Position: Machine Learning Engineer — Model Evaluation & Experimentation
• Type: Contract
• Compensation: $60–$90/hour
• Location: Remote
• Commitment: 35 hours/week
• Contract type: Hourly contractor

Role Responsibilities

• Design tasks by transforming real ML research ideas into well-defined, multi-step tasks.
• Run experiments by implementing changes, executing training experiments, and analyzing results to define correct solutions.
• Explore reinforcement learning concepts such as reward functions and training behavior in task development.
• Evaluate frontier models' performance on tasks and identify areas of improvement.
• Collaborate with researchers to ensure tasks are consistent, rigorous, and fair.

Must-Have

• MSc or PhD in machine learning , computer science , or a related STEM field.
• 1+ years in a research or research-engineering role.
• Experience in training and evaluating ML models and conducting end-to-end experiments.
• Proficiency in Python and Git.
• Strong understanding of large language models and their evaluation.

Preferred

• Basic knowledge of reinforcement learning.
• Experience in AI training, model evaluation, or benchmark/task authoring.