Write machine learning research problems drawn from your own experiment design, ablation and evaluation work, with reference answers. Suits ML researchers with several years of post-degree research who can spot reasoning mistakes others miss.
Sobre la vacante
AfterQuery builds the expert evaluation data that frontier AI labs use to measure and improve their models. We're hiring machine learning researchers as contractors to author and solve the analysis problems those evaluations run on, drawn from your own research: experiment design, ablations, evaluation and model behavior. This role is for researchers with hands-on ML research experience. We're not looking for ML engineers focused on deployment, data engineers or data annotators. Fully remote and async, with no fixed hours.
Responsibilities:
- Author realistic ML research problems drawn from your own work: experiment design, ablations, evaluation and failure analysis
- Work each problem through to a reference answer, with the reasoning behind every step
- Catch the research mistakes that look reasonable but only a practitioner would spot
Required qualifications:
- Master's or PhD in computer science, statistics, mathematics or a related field
- 3+ years of full-time ML research after your degree (a postdoc counts, PhD years don't)
- Hands-on experience designing experiments, running ablations or evaluating models
- Strong Python and a deep learning framework such as PyTorch or JAX
- Able to commit at least 10 hours per week
Fuente oficial: experts.afterquery.com. Los detalles pueden cambiar; la página de la plataforma tiene la última palabra.¿Hay algún error en esta oferta? Avísanos.
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