Machine Learning PhD Researcher (Contract)

Job Type

Flexible Contract (Remote) Job Summary: We are hiring PhD-level Machine Learning Researchers for a contract project that tests whether frontier AI models can handle real research-grade machine learning work. You will create hard command line tasks from your field that a leading AI model cannot solve, then review where and why it fails.

What you will do

• Create command line tasks based on real machine learning research problems.
• Reproduce a result from a recent paper, including the training recipe, data processing and evaluation.
• Find the flaw in an experimental setup, such as a train-test leak, an unfair baseline, a wrong metric or a seed-dependent result.
• Diagnose a failing training run, such as diverging loss, vanishing gradients or silent numerical errors.
• Implement a loss function, optimizer, attention variant or sampling method from its mathematical description.
• Fix or extend an evaluation harness so that a benchmark comparison is valid.
• Build each task’s environment, including the code, data and dependencies, and write a reference solution.
• Write automated tests that check whether a solution is correct, with consistent results on every run (fixed seeds, tolerance-based checks).
• Run your task against a leading AI model, review its attempt, and adjust the task until the model fails because the problem is truly hard, not because the instructions are unclear.

Who we are looking for

• A PhD in machine learning, computer science, statistics, applied mathematics, engineering or another STEM field. PhD candidates and students are welcome to apply.
• Hands-on research or industry experience in machine learning. Ten or more years of experience in machine learning, systems or security is ideal.
• At least one publication, academic or professional.
• Strong experience with PyTorch, JAX or TensorFlow, including debugging real training runs.
• Comfort with Python, the Linux command line, Docker and Git.
• Experience with experimental design, benchmarking or testing AI systems is a plus.
• Experience with LLM training, evaluation or reinforcement learning is a plus.
• Peer review experience, or experience building technical tests or assessments, is an advantage.
• Able to work independently in a remote setting and explain technical findings clearly.

Why Cobalt AI

• Advance frontier AI where it counts. Apply your expertise to the data frontier labs cannot get any other way, where your reasoning directly shapes how the next generation of models solves technical problems.
• Grow professionally. Expand your influence through evaluation projects, advisory roles and research collaborations, while deepening your understanding of how frontier models are trained and assessed.
• Work with a top-tier network. Collaborate with researchers and engineers from leading institutions and labs on high-impact, flexible work.
• Set your own schedule. Flexible 10 to 40 hour weeks that fit around your current role and your life.
• Competitive pay. Paid per accepted task, with higher rates for consistently strong work.

Education

* Doctorate (Required)

Work Location: Remote