Ai Research Scientist - Fully Remote | Up To $200/Hr

Mercor; San Francisco (CA); Hybrid; GBP 154,000 - 205,000; Full time

Job summary

Mercor seeks a Machine Learning Expert for a contract, remote engagement. The role focuses on inference, evaluation, and systems work for a long-horizon AI research project, rather than heavy model training.

You will design tasks to challenge frontier models and explain why problems are hard for AI, collaborating with researchers on LLMs, RL, and GPU inference.

Qualifications

• Current or recent role as an ML research engineer, research scientist, or applied scientist.
• Experience training, fine-tuning, or optimizing models in PyTorch or JAX.
• Deep knowledge in LLMs, GPU inference and performance, RL, or Scientific ML.
• Ability to explain why a problem is hard for a frontier model.

Responsibilities

• Develop challenging ML engineering tasks for a long-horizon AI research project.
• Design tasks that challenge frontier models, solvable in no more than 2 of 8 attempts.
• Focus on inference, evaluation, and systems work on GPU and CPU.
• Clearly explain why each problem is difficult for frontier models.
• Contribute ideas without needing to build or review tasks yourself.

Skills

ML research engineer

research scientist

applied scientist

LLMs

GPU inference and performance

Reinforcement learning

Scientific ML

Explain why problems are hard for a FP

Education

PhD in ML

CS or physics

MSc with strong research output

Tools

PyTorch

JAX

Job description

About the job

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.

Position: Machine Learning Expert
Type: Contract
Compensation: $150–$200/hour
Location: Remote

Role Responsibilities

• Develop challenging machine learning engineering tasks for a long-horizon AI research project.
• Design tasks that challenge frontier models, ensuring they are solved in no more than 2 out of 8 attempts.
• Focus on inference, evaluation, and systems work rather than heavy model training. Tasks run on both GPU and CPU.
• Clearly explain why each problem is difficult for a frontier model.
• Contribute ideas without needing to build or review the tasks yourself.

Qualifications

Must-Have

• Current or recent role as an ML research engineer, research scientist, or applied scientist.
• Experience with training, fine-tuning, or optimizing models in PyTorch or JAX.
• Deep knowledge in at least one area: LLMs, GPU inference and performance, Reinforcement learning (RL), or Scientific ML.
• Ability to explain why a problem is hard for a frontier model.

Preferred

• Experience writing benchmark or evaluation questions.
• PhD in ML, CS, or physics, or an MSc with strong research output.
• Publications.
• Prior Mercor contracts with strong performance.

Application Process (Takes 20–30 mins to complete)

• Upload resume
• AI interview based on your resume
• Submit form

Resources & Support

• For details about the interview process and platform information, please check: https://talent.docs.mercor.com/welcome
• For any help or support, reach out to: [e-mail]

PS: Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.