AI Research Scientist - Fully Remote

Mercor; Toronto; À distance; CAD 120 000 - 180 000; Plein temps

Résumé du poste

Mercor is seeking an ML research engineer to ideate and shape hard research tasks for a leading AI lab. You’ll focus on inference, evaluation and systems work, contributing ideas rather than building every task.

You should personally train or optimize models in PyTorch or JAX and explain why challenges are hard for frontier models. Strong publication or PhD background is welcomed.

Qualifications

• Must have hands-on experience training or fine-tuning models in PyTorch or JAX.
• Experience designing hard ML tasks and evaluating frontier models.
• Ability to explain why problems are hard for frontier models.

Responsabilités

• Propose long-horizon ML tasks for an AI research project in a leading lab.
• Create tasks that stump frontier models, aiming for a solution in 2 of 8 attempts.
• Focus on inference, evaluation and systems work, with GPU and CPU workloads balanced.
• Explain why each problem is hard for frontier models.
• Contribute ideas; implementation and review of tasks are not required.

Connaissances

ML research

PyTorch

JAX

Model fine-tuning

GPU inference

Formation

PhD in ML / CS / Physics

MSc with strong research output

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