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 solid 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 solid 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
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📌 Ml Research Engineer Pytorch Expert Toronto
🏢 Mercor
📍 Toronto