About The Company
Jobrapido is sharing this remote contract opportunity for experienced machine learning professionals interested in contributing to a long-horizon AI research project with a leading AI lab. The project focuses on developing challenging machine learning engineering task ideas that can help assess the capabilities and limitations of advanced AI models. The work is intended for practitioners with substantial research or applied experience who can identify demanding problems at the intersection of machine learning, inference, evaluation, and systems engineering.
About The Role
As a Machine Learning Expert, you will propose concepts for technically rigorous tasks designed to test how consistently frontier AI models can reason about and address complex machine learning engineering challenges. Your contributions will center on the design and explanation of problem ideas, rather than implementation or review of completed tasks. Areas of interest include inference, model evaluation, and systems engineering, with potential task concepts involving both GPU and CPU workloads.
This is a remote, hourly contract engagement offering compensation of $150–$200 per hour. The role is suited to machine learning research engineers, research scientists, and applied scientists who have hands-on experience training, fine-tuning, or optimizing models. You will draw on your technical judgment to identify problems that are meaningful, difficult, and relevant to current machine learning practice, and to explain why advanced models may struggle to solve them reliably.
Qualifications
Applicants should have current or recent experience as a machine learning research engineer, research scientist, or applied scientist. Hands-on experience personally training, fine-tuning, or optimizing models using PyTorch or JAX is required. Candidates should have deep knowledge in at least one relevant technical area, such as large language models, including pretraining, alignment, and evaluation; GPU inference and performance, including tools and technologies such as vLLM, Triton, CUDA, or profiling; reinforcement learning; or scientific machine learning applied to fields such as weather, climate, physics, or biology.
You should be able to explain clearly why a proposed machine learning problem is challenging for frontier AI models.
Experience developing benchmark or evaluation questions is preferred. A PhD in machine learning, computer science, or physics is advantageous, as is an MSc accompanied by strong research output. Relevant research publications and previous Mercor contract experience with strong performance are also considered assets. Applicants should bring sound technical communication skills and the ability to describe complex problem concepts in a clear, structured way.
Responsibilities
Develop ideas for challenging machine learning engineering tasks that could support the AI research project. Design problem concepts that are difficult for frontier models to solve consistently, while keeping them grounded in real technical challenges. Focus on inference, evaluation, and systems engineering, including ideas that involve GPU and CPU workloads.
For each proposed concept, explain the technical reasoning behind its difficulty and identify the capabilities it is intended to test. Contribute task concepts based on your expertise; building, implementing, or reviewing the tasks themselves is not required for this opportunity.
Benefits
This remote contract offers flexible hourly engagement and compensation of $150–$200 per hour. It provides an opportunity to contribute specialized machine learning expertise to a long-horizon research effort with a leading AI lab. The work is focused on intellectually demanding problems relevant to frontier AI, while allowing experts to contribute ideas without taking on task implementation or review responsibilities. The application process was not specified in the provided listing.
Equal Opportunity
Jobrapido welcomes qualified professionals with relevant skills and experience to apply. Candidates will be considered based on their expertise, qualifications, and ability to contribute to the work described. Applicants from all backgrounds are encouraged to pursue the opportunity.