We’re looking for experienced computational biologists and bioinformaticians to help train AI models. You’ll design realistic analysis tasks from your own practice, run them through frontier AI systems, and grade what comes back against a professional standard.
The models can talk fluently about biological data analysis. What they can’t yet do reliably is the real work: QC an RNA-seq count matrix, prioritize variants, diagnose a batch effect, or audit a pipeline. Your judgment of when a result is real becomes the benchmark those models are measured against.
What you’ll actually do
- Design realistic analysis tasks from your own workflows: the scenario, the prompt, and the files an agent would need (count matrices, sample sheets and metadata, VCFs, pipeline logs and QC output, analysis notebooks).
- Run tasks through frontier AI agents and grade the deliverable (an analysis report, annotated table, figure set, or notebook) against the standard you’d hold a colleague to.
- Write clear grading rubrics (the right normalization, the right multiple-testing correction, the right biological reading) and explain why a response passes or fails.
- Flag concrete failures with evidence: batch effects and confounders ignored, wrong statistical test or uncorrected p-values, misused reference version, silently dropped samples, or code that doesn’t do what the narrative claims.
Problems draw on whatever you know best:
- Genomics and variant interpretation.
- Bulk and single-cell transcriptomics.
- Proteomics, metabolomics, and multiomics integration.
- Population and statistical genetics.
- Machine learning applied to biology.
Roles this fits
Common backgrounds: Bioinformatics Scientist, Genomics Data Scientist, Computational Biologist.
What we look for
- 3+ years hands-on analyzing real biological data in industry, an academic lab, or a research institute (counted after undergraduate study).
- You write, debug, and can explain your own analysis code in Python and/or R (at least one required, both preferred).
- Depth in at least one of: genomics and variant interpretation; bulk or single-cell transcriptomics; proteomics / metabolomics / multiomics; population and statistical genetics; ML applied to biology; clinical genomics; metagenomics or phylogenetics.
- You’ve owned a multistep analysis end to end, from raw or messy data to final interpretation, and can judge whether a result is real (normalization, batch effects, multiple-testing correction, biological interpretation).
- Master’s or PhD (or current PhD candidate) in biology, bioinformatics, biostatistics, computer science, or a related field, completed in the U.S., Canada, Europe, or the UK.
- Clear written English, comfort with ambiguity, and familiarity with AI/LLM tools like Claude or ChatGPT.
- Based in the United States, Canada, or the UK (Ireland and Australia may also be accepted).
Compensation
Up to $125+/hr depending on task difficulty and specialization. Many contributors add $100k–$200k a year; some make it their full-time income.
About DataAnnotation
DataAnnotation is where 1M+ experts train the world’s leading AI models. Hundreds of millions paid to contributors to date, and the average contributor stays 5+ years. Flexible, remote, and always project-available.
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StudySmarter Expert Advice🤫
We think this is how you could land Epidemiologist - AI Trainer
✨Join Medical Networks
Get involved in local and national medical associations, like the Royal College of Physicians. These networks can help you connect with mentors and colleagues who might know of openings, plus they're great for staying updated on the latest in medicine and clinical practices.
✨Attend Healthcare Conferences
Make it a habit to attend medical conferences and seminars. These events are fantastic for networking and often feature job boards where hospitals and clinics post their openings. Plus, it’s a good way to get your face recognised in the industry.
✨Leverage Your Medical School's Resources
Don’t forget to use your medical school’s career services! They usually have great connections with hospitals and clinics looking to fill full-time physician positions and can help you with mock interviews or networking events.
✨Apply Directly Through Hospital Websites
When you're ready to apply, make sure to check the careers section of hospital websites directly. Hospitals like DataAnnotation often post their job openings there first, and applying directly can sometimes give you an edge over other applicants.
Some tips for your application 🫡
Show Off Your Credentials:When applying for a physician role, make sure to highlight your medical qualifications and any special certifications you have. We want to see your medical degree, residency training, and any additional certifications that set you apart, like specialised training in paediatrics or geriatrics. Don’t leave out those all-important letters after your name!
Tailor Your CV to Medicine:Your CV should reflect your experience and skills as a doctor. Be specific about your clinical rotations, medical practice, and any research work you've done – we love seeing quantifiable impacts, so mention how you've improved patient care or led successful treatment plans.
Craft a Personal Statement with Heart:In your cover letter, let us know why you chose medicine and what drives your passion for patient care. Personal stories about transformative experiences—like a memorable patient encounter or a moment that defined your career path—can really make you shine. Remember, we're looking for someone who genuinely cares about their patients!
Demonstrate Continuous Learning:In the medical field, showcasing your commitment to continuous learning is essential. Mention any recent courses, workshops, or conferences you’ve attended, and highlight your willingness to stay updated on medical advancements. This shows us that you’re not just a physician; you're a lifelong learner, ready to adapt and grow.
How to prepare for a job interview at DataAnnotation
✨Get Clinical Knowledge Sharp
Brush up on your clinical knowledge, particularly in areas relevant to the role at DataAnnotation. Expect questions about diagnosis, treatment plans, and patient management. Be ready to discuss case studies or even do some role-playing with hypothetical patient scenarios for a more practical assessment.
✨Know Your Procedures and Protocols
Familiarise yourself with the latest procedures and protocols that are specific to the field of medicine you’re applying to. Whether it’s surgical techniques or patient care protocols, being able to discuss these confidently can really set you apart in the interview at DataAnnotation.
✨Show You're a Team Player
In the medical field, collaboration is key. Have examples ready that showcase your teamwork and communication skills. Talk about how you've worked with nurses, specialists, or other doctors in the past to provide the best care for your patients. This shows you're ready to integrate into the team at DataAnnotation.
✨Stay Current with Medical Trends
Make sure you’re clued up on the latest medical trends, research, and innovations. Not only does this prepare you for technical questions, but it also shows your passion for the field. Be ready to discuss how these trends might affect your practice at DataAnnotation.
