At a Glance
- Tasks: Design challenging ML tasks for cutting-edge AI research projects.
- Company: Join a leading AI lab focused on innovative machine learning solutions.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic team environment with a focus on collaboration and innovation.
- Why this job: Contribute to groundbreaking AI research and tackle complex problems.
- Qualifications: Experience in ML engineering with expertise in PyTorch or JAX.
The predicted salary is between 135000 - 165000 $ per year.
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.
ML Research Engineer in Toronto employer: Mercor
Mercor is an exceptional employer that fosters a dynamic and innovative work culture, connecting top-tier talent with leading AI research labs. With the flexibility of remote work, employees enjoy a supportive environment that encourages professional growth and collaboration across diverse teams. The competitive compensation and the opportunity to engage in high-impact projects make Mercor a rewarding place for legal experts looking to make a meaningful contribution.
