Job Type
Flexible Contract (Remote) Cobalt is hiring current postdoctoral researchers and PhD students with hands-on flow cytometry experience to review tasks and environments used to test whether frontier AI models can carry out real cell biology and cytometry work. Your job is to make sure each task is scientifically correct, fair, and actually measures what it claims to measure before it is accepted. If you enjoy spotting the flaw in a gating strategy, the missing control in a panel, or the error in someone else's compensation, this role is built around that skill. What you will do:
• Review flow cytometry tasks end to end, including the task description, raw FCS files and sample metadata, panel and staining information, analysis code, reference solution, and automated graders, and confirm that each part is correct and consistent with the others.
• Reproduce reference analyses yourself to confirm that a sound analysis passes and a flawed one fails. Because population frequencies legitimately vary between analysts, instruments, and gate placements, you will check that graders use tolerances that are clearly justified rather than rewarding one exact gate.
• Look for ways a model could pass a task without doing the intended work, such as reading the answer from file names or metadata, guessing typical population frequencies from the literature, or satisfying a grader that checks output format but not the biology, and document each issue so the task author can fix it.
• Review how a frontier AI model attempted each task and judge whether its failure reflects a real gap in scientific capability, or instead reflects ambiguous wording, missing controls, a broken dataset, or an unfair grader.
• Write clear,



specific feedback to task authors and work with them through revisions until the task meets the acceptance standard.
• Track recurring problems across tasks and help improve the review checklist and quality standards over time. Example tasks you might review include analyzing a flow cytometry dataset to identify and quantify cell populations, troubleshooting a flawed analysis or experimental setup, designing a staining panel and choosing appropriate controls, interpreting the output of a high-dimensional analysis, and running a sound statistical comparison between experimental conditions. Who we are looking for:
• Currently enrolled in a Masters or PhD program, or working as a postdoctoral researcher, in immunology, cell biology, molecular biology, hematology, biomedical engineering, or a closely related field.
• Hands-on flow cytometry experience through research, a thesis, or industry work, including running samples, designing panels, and gating data. You should be comfortable reading someone else's gating strategy and saying whether it is correct.
• Strong working knowledge of at least one analysis platform, such as FlowJo, FCS Express, or OMIQ. Experience analyzing cytometry data in R or Python (for example flowCore, CATALYST, or FlowKit) is a significant plus.
• A solid grasp of core cytometry concepts, including compensation and spectral unmixing, fluorescence-minus-one and viability controls, doublet exclusion, antibody titration,



and how instrument settings affect results.
• A solid grasp of experimental design and statistics, including biological versus technical replicates, donor-to-donor variability, and when a difference between two conditions is meaningful.
• Careful, direct written communication. Much of the job is explaining precisely what is wrong and why. Strong advantages:
• Experience working in or running a flow cytometry core facility, or training other researchers on instruments and analysis.
• Experience with spectral cytometry (for example Cytek Aurora), mass cytometry (CyTOF), or cell sorting.
• Experience with clinical flow cytometry, such as immunophenotyping for hematologic malignancies or measurable residual disease testing.
• Experience with single-cell genomics methods that pair with cytometry, such as CITE-seq or single-cell RNA sequencing.
• A publication or preprint using flow cytometry, which we value but do not require.
• Experience as a teaching assistant who wrote assignments or graded lab work, or as a peer reviewer for a journal. Why Cobalt AI:
• Work on frontier AI evaluation. Your judgment directly determines which tasks are used to measure what leading models can and cannot do in cell biology.
• Build skills that matter for your research. Reviewing many tasks and model attempts gives you a close view of how frontier models reason about experimental data, and where they break.
• Fit the work around your research. Set your own schedule, with versátil weeks of 10 to 40 hours that can scale down during busy lab or academic periods.
• Grow with us. Strong reviewers can move into task design, project leadership, and research collaborations on future projects.

📌 Cell Biology Scientist, AI Evaluation (México)
🏢 COBALT
📍 México