Posted 2 months ago
Member of Technical Staff - ML Data
@ Veeda AI
Toronto, Ontario
Overview
Veeda AI is hiring a Member of Technical Staff for ML data to design and validate machine learning methods for data selection, enrichment, annotation and quality assessment for multimodal Physical AI world models. The role covers the full lifecycle of methods on large-scale real-world and synthetic datasets, with a focus on research and evaluation.
Responsibilities:
- Design and validate ML methods for data selection and annotation
- Own the full lifecycle from problem formulation to deployment
- Build pipelines for large-scale real and synthetic data
- Measure data quality and its impact on model performance
- Produce and evaluate captions, depth maps and segmentation masks
Requirements summary:
- Master's or PhD in CS, engineering or related field, or equivalent experience
- Peer-reviewed publications or substantial research contributions
- Strong Python and PyTorch skills
- Experience training and evaluating ML models
- Software engineering focused on reproducibility
Tools & skills
Source and dates
Employer career site / ATS feed · ashby
- Employer posting date
- 2026-09-08
- First seen by our source
- 2026-10-08
- Source record updated
- 2026-10-22
These dates describe the source record, not a live verification that the employer is still hiring. Check the original posting.
Job details
| Seniority | Mid level |
| Department | Data & analytics |
| Hours per week | 40 (estimated) |
| Work setting | Office |
| Position | Mostly sitting |
| Physical demands | physical low · computer high |
| Languages | English |
| Education | Master’s degree (required), Doctorate (required) |
| Fields of study | Computer Science, Engineering |
Summary and details written by AI from the posting; the employer’s posting is the authority.
Veeda AI
Saved company profile
Historical company information from a saved The Jobs API profile (LinkedIn/Crunchbase fields). It is not live-verified; figures may have changed. Missing values remain unknown.
Scaling Physical AI through Interactive Learning in Simulated Reality
Full company description
Scaling Physical AI through Interactive Learning
- People associated on LinkedIn
- 3
- Other recorded names
- Veeda AI
- Profile identifies a recruitment agency
- No
Scaling Physical AI through Interactive Learning
| Field | Value |
|---|---|
| Industry | Artificial Intelligence |
| Company size | 11-50 employees |
| Organization type | Privately Held |
| Year founded | 2026 |
| Open jobs on Jobsy | 9 |
| View page |
Member of Technical Staff - ML Data
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.
Journal
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