Member of Technical Staff - ML Data

Veeda AI; Zürich; On-site; CHF 120,000 - 180,000; Full time

Job summary

Veeda AI seeks a talented ML/Data scientist to own the full lifecycle of data-driven methods. You will design, validate, and deploy ML approaches for large-scale, real-world datasets, including synthetic data generation and annotation tasks.

You will collaborate with researchers and engineers to improve data quality and model performance, applying rigorous experiments and reproducible software practices in a fast-moving environment.

Qualifications

• Master's degree or PhD in CS, Engineering, or related field, or equivalent hands-on experience.
• Proven ability to develop ML methods with rigorous experimental validation.
• Experience owning the full lifecycle of an ML method: design, data application, evaluation, iteration.
• Strong Python and PyTorch skills; ability to train and adapt models.
• Design controlled experiments with meaningful metrics and error analysis.

Responsibilities

• ML methods for data: design, develop, validate data selection, enrichment, annotation, quality assessment.
• End-to-End ownership: from problem formulation through deployment and improvement.
• Large-Scale application: build workflows for large-scale datasets and synthetic data pipelines.
• Evaluation & experimentation: measure data quality and impact on model performance.
• Iterative improvement: use failure analysis to improve data-processing methods.
• Annotation: produce labels such as captions, poses, depth maps, and segmentation masks.
• Research collaboration: partner with researchers and engineers to develop data solutions.

Skills

Python

PyTorch

Experiment design

Data analysis

Software engineering

Education

Master's degree or PhD

Tools

Python

PyTorch

Distributed systems

Job description

About Us

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.

About Us

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.

Responsibilities

• 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.

Requirements

• 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.

Nice to Have

• 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.