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 labDesign tasks hard enough to stump frontier models: a solid task is solved in no more than 2 out of 8 attemptsLean toward inference, evaluation and systems work rather than heavy model training. About half of the tasks run on GPU and half are CPU-onlyExplain clearly why each problem is hard for a frontier modelYou mainly contribute ideas. You do not need to build or review the tasks yourselfMust-haves Current or recent role as an ML research engineer, research scientist or applied scientistHands-on experience personally training,



fine-tuning or optimizing models in Py Torch or JAXDeep knowledge of at least one of these areas: LLMs: pretraining, alignment, evalsGPU inference and performance: v LLM, Triton, CUDA, profilingReinforcement learning (RL)Scientific ML: weather and climate, physics, biologyAble to explain why a problem is hard for a frontier modelNice-to-haves Experience writing benchmark or evaluation questions (a big plus)Ph D in ML, CS or physics, or an MSc plus strong research outputPublicationsPrior Mercor contracts with strong performanceNot a fit Software or full-stack engineers whose AI experience is mainly building Lang Chain or RAG applicationsBackgrounds limited to AI trainer, rater or annotator work#J-18808-Ljbffr

📌 Ml Research Engineer - Pytorch Expert (Toronto)
🏢 Mercor
📍 Toronto