Machine Learning PhD Student Contributor

Job Description: About the role:
Cobalt is seeking PhD-qualified machine learning researchers with direct experience designing, running, and evaluating original ML research. This opportunity is suited to researchers who have worked in academic ML labs, industry research groups, or frontier lab environments, and who understand how technical claims are established, tested, and supported by evidence.
You may currently work, or have previously worked, as a PhD candidate, Postdoctoral Researcher, Research Scientist, Research Engineer, Applied Scientist, Member of Technical Staff, or in a related role.
You do not need prior experience in data annotation or model evaluation. You must, however, have contributed meaningfully to at least one substantive ML research output, and you must be comfortable reading papers, interpreting experimental results, and judging whether stated conclusions follow from the underlying evidence.

What you'll do:
Depending on the project, you may:

Produce written reasoning traces on hard ML problems, capturing how you reach a solution rather than only the solution itself, and draft expert reference answers to technical questions
Author novel problems in your subfield that have verifiable or defensible correct answers
Evaluate model-generated technical content: compare and rank responses, articulate what makes the stronger one stronger, and identify the specific step at which a chain of reasoning breaks down
Assess whether stated conclusions are supported by the underlying derivation, code, or experimental evidence
Design rubrics and partial-credit criteria for scoring multistep technical tasks, and contribute subject-matter expertise to benchmark and dataset development

Projects follow their own annotation guidelines and quality standards, and you will work with feedback from reviewers and lab research teams.

Required qualifications:

PhD, completed or in progress, in machine learning, computer science, statistics, mathematics, physics, or a closely related quantitative discipline, with research that is substantially ML focused
Direct experience authoring, co-authoring, or substantively contributing to at least one ML research output, such as a peer-reviewed paper, preprint, thesis chapter, or comparable technical artifact
Demonstrated depth in at least one area, for example optimization, reinforcement learning, language model training and post-training, learning theory, probabilistic methods, computer vision, natural language processing, or systems for ML
Ability to interpret papers, derivations, code and experimental results, and to explain your reasoning clearly in writing
Strong attention to detail, a commitment to factual accuracy, and the ability to work independently to agreed timelines

Why join Cobalt AI:

Advance frontier AI where it counts. Apply your research expertise to the data that frontier labs cannot obtain any other way, where your reasoning directly shapes how the next generation of models works through 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 from leading institutions and labs on high-impact, flexible work.
Set your own schedule. Flexible 10 to 40 hour weeks that fit around your research position and your life.
Competitive pay. Rates vary by project and are determined by a number of factors, including scope, skillset, and experience.