Veeda AI is building the next generation of multimodal foundation world models for Physical AI. We're a small, fast-moving team of engineers and researchers from leading AI labs, tackling some of the most challenging problems at the intersection of AI, robotics, and embodied intelligence. If you're excited about pushing the boundaries of what's possible with Physical AI, you'll have the opportunity to make an outsized impact from day one.
ML Methods for Data: Design, develop, and validate ML methods for data selection, enrichment, annotation, and quality assessment.
End-to-End Ownership: Own the full lifecycle, from problem formulation through deployment and continuous improvement.
Large-Scale Application: Build reliable workflows for processing large-scale real-world datasets and scalable pipelines for generating synthetic data.
Evaluation & Experimentation: Measure data quality and assess its impact on model performance.
Iterative Improvement: Use failure analysis and feedback to improve data-processing methods.
Annotation: Produce and evaluate labels such as captions, camera poses, depth maps, and segmentation masks.
Research Collaboration: Partner with researchers and engineers to develop effective data solutions.
Master’s or Ph.D. in Computer Science, Engineering, or a 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 it 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 an emphasis on reproducibility, reliability, and maintainable code.
Publications in computer vision, robotics, or related machine learning fields, with a 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 a strong focus on idempotency, backfills, and schema evolution.
Develop scalable methods and data pipelines to curate fleet-scale driving data, uncover safety-critical scenarios, and build benchmarks that improve autonomous driving and foundation models.
Develop and evaluate machine learning models for therapeutic antibody discovery, then turn promising approaches into reliable platform services and AI features used in scientific workflows.
Develop foundation-model architectures, training methods, and scalable data systems for general-purpose robots, translating multimodal machine learning research into robust policies and measurable real-world performance.
Develops and productionizes machine learning and GenAI solutions, improving data pipelines and existing code while collaborating across data and application teams to deliver reliable, scalable capabilities.
As a Machine Learning Engineer, you will deliver impactful AI solutions, operationalizing machine learning models and collaborating with cross-functional teams to ensure project success.
