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 Py Torch 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 Py Torch or JAX
Deep knowledge of at least one of these areas: LLMs: pretraining, alignment, evals
GPU inference and performance: v LLM, 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)
Ph D in ML, CS or physics, or an MSc plus robust research output
Publications
Prior Mercor contracts with strong performance
Not a fit Software or full-stack engineers whose AI experience is mainly building Lang Chain or RAG applications
Backgrounds limited to AI trainer, rater or annotator work

📌 Ml research engineer (Toronto)
🏢 Mercor
📍 Toronto