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

📌 ML Research Engineer (Toronto)
🏢 Mercor
📍 Toronto