ML Research Engineer - PyTorch Expert

Mercor; Greater London; On-site; GBP 90,000 - 140,000; Full time

Job summary

Mercor is seeking an ML research engineer, research scientist, or applied scientist who has personally trained, fine-tuned, or optimized models in PyTorch or JAX. The role focuses on proposing hard, long-horizon tasks for an AI lab, prioritizing inference/evaluation and systems work, with an emphasis on explaining model difficulties. Strong research background preferred.

Qualifications

• Hands-on training/fine-tuning/optimizing models in PyTorch or JAX.
• Ability to explain why problems are hard for frontier models.
• Experience with ML research tasks design and evaluation.

Responsibilities

• Generate ideas for hard ML engineering tasks for long-horizon AI projects.
• Design tasks that challenge frontier models; a strong task solves in few attempts.
• Focus on inference, evaluation, and systems work; balance GPU/CPU workloads.
• Explain why each problem is hard for a frontier model; mainly contribute ideas, not implement.

Skills

Hands-on model training

Explain frontier-model hardness

Plan design for long-horizon AI tasks

Education

PhD in ML/CS/Physics or MSc with strong research output

Tools

PyTorch

JAX

vLLM

Triton/CUDA

Job description

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