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Veeda AI is building multimodal foundation models for Physical AI, combining advances in AI, robotics, and embodied intelligence. The company is a small, fast-moving team of engineers and researchers from leading AI labs.
In this Member of Technical Staff role, you will own the full lifecycle of ML methods for data, from problem formulation through deployment and continuous improvement. Your responsibilities include:
- Design, develop, and validate ML methods for data selection, enrichment, annotation, and quality assessment
- Own end-to-end workflows for processing large-scale real-world datasets and generating synthetic data at scale
- Build reliable, scalable pipelines for data processing
- Measure data quality and assess its impact on model performance through rigorous experimentation
- Produce and evaluate labels such as captions, camera poses, depth maps, and segmentation masks
- Use failure analysis and iterative feedback to continuously improve data-processing methods
- Collaborate with researchers and engineers to develop effective data solutions
This is a high-impact individual contributor role where you'll have outsized influence from day one, working on foundational problems in Physical AI.
REQUIREMENTS:
- Master's or Ph.D. in Computer Science, Engineering, or related technical field, or equivalent hands-on experience
- Demonstrated ability to develop original ML methods, evidenced by peer-reviewed publications or substantial research contributions with rigorous experimental validation
- Experience owning the full lifecycle of an ML method: designing and implementing the approach, applying it to large-scale data, evaluating results, and improving through successive iterations
- Strong Python and PyTorch skills, with experience training, adapting, and evaluating machine learning models
- Ability to design controlled experiments, establish meaningful metrics, and analyze errors to guide improvements
- Strong software engineering skills with emphasis on reproducibility, reliability, and maintainable code
NICE TO HAVE:
- Publications in computer vision, robotics, or related machine learning fields with substantial personal contribution
- Experience scaling ML inference and data processing with distributed computing tools
- Experience optimizing large-scale inference or data-processing workflows
- Experience building and operating distributed data pipelines over hundreds of terabytes, with focus on idempotency, backfills, and schema evolution
