Machine Learning Expert (Remote | $150–$200/hr)
Position: Machine Learning Expert
Type: Hourly Contract
Compensation: $150–$200 per hour
Location: Remote
About the Opportunity
This opportunity is for experienced machine learning research engineers, research scientists, and applied scientists to contribute challenging machine learning engineering task ideas for a long-horizon AI research project with a leading AI lab. The focus is on designing problems that test the limits of frontier AI models, particularly in inference, evaluation, and systems engineering.
Key Responsibilities
- Develop ideas for challenging machine learning engineering tasks for an AI research project.
- Design problems that are difficult for frontier models to solve consistently.
- Focus on inference, evaluation, and systems engineering, with tasks involving both GPU and CPU workloads.
- Explain clearly why each proposed problem is challenging for advanced AI models.
- Contribute task concepts without needing to build or review the tasks themselves.
Required Qualifications
- Current or recent experience as an ML research engineer, research scientist, or applied scientist.
- Hands-on experience personally training, fine-tuning, or optimizing models using PyTorch or JAX.
- Deep knowledge of at least one of the following areas:
- Large language models (LLMs), including pretraining, alignment, and evaluations.
- GPU inference and performance, including vLLM, Triton, CUDA, or profiling.
- Reinforcement learning (RL).
- Scientific machine learning, including weather, climate, physics, or biology.
- Ability to explain why specific machine learning problems are difficult for frontier models.
Preferred Qualifications
- Experience developing benchmark or evaluation questions.
- PhD in machine learning, computer science, or physics, or an MSc with strong research output.
- Relevant research publications.
- Previous Mercor contract experience with strong performance.
Compensation
- $150–$200 per hour.
- Remote hourly contract engagement.
Application Process
The application process was not specified in the provided listing.