Pay: $150 – $200/hour

Commitment: Hourly, ~20 h/week

Field: Data Analysis

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 solid 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

📌 Machine Learning Engineer - PhD in Machine Learning (Canada)
🏢 Mercor
📍 Canada