Powering generalist robotics and world models with multi-million-hour annotated egocentric data, hand tracking, upper-body/whole-body motion capture, tactile sensing, and simulation for OpenAI, Google DeepMind, Meta, Figure, 1X, Skild, Genesis AI, and Dyna... Backed by South Park Commons.
About the role
We are looking for an Applied Scientist/Research Engineer to improve the quality of training data and evaluation systems used to develop advanced AI models. In this role, you will investigate how data quality, dataset composition, and evaluation methods influence model performance. You will design experiments, develop data curation and scoring methods, analyze model failures, and build tools that help us identify the data most valuable for training and improving AI systems. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Experience in applied ML research, data science, AI evaluation, or a related technical field
- Strong Python skills and experience with relevant ML frameworks and data processing tools
- Experience with one or more of the following: training data curation, dataset optimization, model evaluation, benchmark development, active learning, or post-training
- Strong experimental design and analytical skills, with the ability to connect data characteristics to model performance
- Experience developing technical solutions, research methods, or automated pipelines rather than only performing annotation or data labeling
- Ability to communicate research findings clearly and translate them into actionable improvements
More about this role
Maxinsights is a pioneer in embodied robotics data solutions. We provide end-to-end data infrastructure, from multimodal data collection and annotation to algorithm-ready dataset delivery, for leading robotics foundation model companies and embodied AI labs worldwide.
We are looking for an Applied Scientist/Research Engineer to improve the quality of training data and evaluation systems used to develop advanced AI models.
In this role, you will investigate how data quality, dataset composition, and evaluation methods influence model performance. You will design experiments, develop data curation and scoring methods, analyze model failures, and build tools that help us identify the data most valuable for training and improving AI systems.
This role is ideal for someone with a strong background in applied ML research, data-centric AI, or model evaluation who enjoys turning research insights into practical improvements.
Develop methods for training data curation, quality scoring, dataset selection, and data mixing.
Design evaluation frameworks and benchmarks to measure model capabilities and identify performance gaps.
Analyze model failures to understand how training data affects...
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