Machine Learning Expert

**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 strong 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