Machine Learning Expert

To qualify you must work as an ML research engineer, research scientist or applied scientist, and have personally trained, fine-tuned or optimized models in PyTorch or JAX.

What you'll do

• Come up with ideas for hard machine learning engineering tasks ("plans") for a long-horizon AI research project with a leading AI lab
• Design tasks hard enough to stump frontier models: a strong task is solved in no more than 2 out of 8 attempts
• Lean toward inference, evaluation and systems work rather than heavy model training. About half of the tasks run on GPU and half are CPU-only
• Explain clearly why each problem is hard for a frontier model
• You mainly contribute ideas. You do not need to build or review the tasks yourself

Must-haves

• Current or recent role as an ML research engineer, research scientist or applied scientist
• Hands-on experience personally training, fine-tuning or optimizing models in PyTorch or JAX
• Deep knowledge of at least one of these areas:
• LLMs: pretraining, alignment, evals
• GPU inference and performance: vLLM, Triton, CUDA, profiling
• Reinforcement learning (RL)
• Scientific ML: weather and climate, physics, biology
• Able to explain why a problem is hard for a frontier model

Nice-to-haves

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

Not a fit

• Software or full-stack engineers whose AI experience is mainly building LangChain or RAG applications
• Backgrounds limited to AI trainer, rater or annotator work