Publié il y a 2 mois
Member of Technical Staff - ML Data
@ Veeda AI
Toronto, Ontario
En bref
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.
Responsabilités :
- 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
Résumé des exigences :
- 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
Outils et compétences
Source et dates
Site carrière / système de candidatures · ashby
- Date publiée par l’employeur
- 2026-09-08
- Première détection par notre source
- 2026-10-08
- Mise à jour du dossier source
- 2026-10-22
Ces dates décrivent le dossier source, pas une vérification en direct du recrutement. Consultez l’offre originale.
Détails du poste
| Niveau | Intermédiaire |
| Domaine | Données et analytique |
| Heures par semaine | 40 (estimé) |
| Milieu de travail | Bureau |
| Position | Surtout assis |
| Exigences physiques | physique faible · ordinateur élevé |
| Langues | English |
| Formation | Maîtrise (exigé), Doctorat (exigé) |
| Domaines d’études | Computer Science, Engineering |
Résumé et détails rédigés par l’IA à partir de l’offre; l’offre de l’employeur fait foi.
Veeda AI
Profil d’entreprise sauvegardé
Renseignements historiques provenant d’un profil The Jobs API sauvegardé (champs LinkedIn/Crunchbase). Ils ne sont pas vérifiés en temps réel; les chiffres peuvent avoir changé. Les valeurs manquantes restent inconnues.
Scaling Physical AI through Interactive Learning in Simulated Reality
Description complète de l’entreprise
Scaling Physical AI through Interactive Learning
- Personnes associées sur LinkedIn
- 3
- Autres noms enregistrés
- Veeda AI
- Le profil indique une agence de recrutement
- Non
Scaling Physical AI through Interactive Learning
| Champ | Valeur |
|---|---|
| Secteur | Artificial Intelligence |
| Taille | 11-50 employees |
| Type d’organisation | Privately Held |
| Fondée en | 2026 |
| Offres sur Jobsy | 10 |
| Voir la 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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