Machine Learning Expert

Mercor; United States; À distance; USD 180 000 - 240 000; Plein temps

Résumé du poste

Mercor seeks an ML research engineer, research scientist or applied scientist to generate hard, long-horizon task ideas for a leading AI lab. You will prioritize inference, evaluation and systems work, with tasks split evenly between GPU and CPU execution, and explain why each problem challenges frontier models.

You primarily contribute ideas and do not need to build or review the tasks, focusing on clear explanations of model hardness and research direction impact.

Qualifications

• Current or recent role as 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 LLMs, GPU inference and performance, RL, or scientific ML.
• Able to explain why a problem is hard for a frontier model.

Responsabilités

• Come up with ideas for hard machine learning engineering tasks for a long-horizon AI research project.
• Design tasks hard enough to stump frontier models; a strong task should be solved in few attempts.
• Lean toward inference, evaluation and systems work rather than heavy model training.
• Explain clearly why each problem is hard for a frontier model.
• You mainly contribute ideas and do not need to build or review the tasks.

Connaissances

ML research engineer

Research scientist

Applied scientist

PyTorch

JAX

LLMs knowledge

GPU inference & performance

Reinforcement learning

Scientific ML

Explain constraints for frontierModels

Formation

PhD in ML, CS or physics

MSc + strong research output

Outils

PyTorch

JAX

Description du poste

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