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Added 4 mins ago
Underground Heavy Equipment Technician
B2Gold - Nunavut
NU · Nunavut, Canada
Full-time
B2Gold is a responsible international senior gold producer headquartered in Vancouver, Canada. Founded in 2007, today, B2Gold has operating gold mines in Mali, Namibia and the Philippines,…
Added 4 mins ago
Senior Geologist, Geotechnical
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$123,000 to $145,000 CAD / year
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Autodesk

Senior Machine Learning Developer, 3D Geometry & Multimodal AI

Toronto, ON, Canada

Pay: For Canada based roles, we expect a starting base salary between $123,000 and $180,400.
Job type: Full-time
Apply by: Oct 24, 2026
Posting language: English · French
Posted: Sep 24, 2026

Apply About employer

Applications open in a new tab.

Original job description

English section of this bilingual listing.

Job Requisition ID #

26WD99904

L'affichage de poste en français suivra / The French job posting follows.

26WD99904, Machine Learning Developer 3D Geometry & Multimodal AI

Position Overview

We are seeking a Senior Machine Learning Developer to design, develop, and deploy machine learning solutions for next-generation AI-powered capabilities within Autodesk products. The ideal candidate has expertise across the machine learning lifecycle, including research, experimentation, data pipelines, model training, evaluation, MLOps, and production deployment.

You will work closely with researchers and developers to develop innovative AI solutions involving 3D geometry, multimodal AI, and generative AI, helping transform research into production-ready product capabilities. You enjoy solving complex technical problems, exploring new ideas, and collaborating across teams to deliver scalable, high-quality machine learning solutions.

Responsibilities

Design, develop, and optimize machine learning models for AI-powered product features involving 3D geometry, multimodal AI, and generative AI
Build and maintain scalable data pipelines and machine learning workflows for data preparation, model training, evaluation, and inference
Work with complex datasets and representations for model training, evaluation, and analysis
Design and implement evaluation methodologies, benchmarks, and experiments to measure model performance, robustness, and quality
Collaborate with researchers and developers to transform experimental ideas into scalable, production-ready product capabilities
Build and maintain MLOps workflows supporting model versioning, deployment, monitoring, and continuous improvement
Analyze model performance, identify failure modes, and implement improvements to enhance model accuracy, reliability, and efficiency
Document and present technical designs, experimental results, and findings to collaborators and leadership

Minimum Qualifications

Bachelor’s or Master’s degree in Computer Science, Machine Learning, Engineering, or equivalent industry experience
5+ years of professional experience developing machine learning solutions
Experience working across the machine learning lifecycle, including research, data pipelines, model training, evaluation, MLOps, and production deployment
Experience working in research or experimental environments with evolving requirements
Proficiency with modern deep learning frameworks (e.g., PyTorch, Hugging Face)
Experience with computational geometry and 3D data (e.g., meshes, B-Rep models)
Experience working with complex data representations, including 2D and 3D geometry
Experience with version control, reproducibility, and deploying machine learning models
Hands-on experience using modern AI/GenAI tools to improve software development, experimentation, or machine learning workflows
Excellent written and verbal communication skills

Preferred Qualifications

Advanced degree (MSc/PhD) in Machine Learning, Computer Science, or a related field
Experience with LLMs or generative AI systems (e.g., fine-tuning, prompting, evaluation, or agentic workflows)
Experience scaling machine learning training and data pipelines (e.g., using Ray or similar frameworks)
Experience with cloud platforms and services (e.g., AWS, Azure, Google Cloud Platform)
Knowledge of the design, manufacturing, AEC, or media & entertainment industries
Experience with Autodesk or similar products (e.g., CAD, CAE, CAM)

We take great pride in our culture here at Autodesk – it’s at the core of everything we do. Our culture guides the way we work and treat each other, informs how we connect with customers and partners, and defines how we show up in the world.

When you’re an Autodesker, you can do meaningful work that helps build a better world designed and made for all. Ready to shape the world and your future? Join us!

Salary transparency

Salary is one part of Autodesk’s competitive compensation package. For Canada based roles, we expect a starting base salary between $123,000 and $180,400. Offers are based on the candidate’s experience and geographic location, and may exceed this range. In addition to base salaries, our compensation package may include annual cash bonuses, commissions for sales roles, stock grants, and a comprehensive benefits package.

Belonging

We take pride in cultivating a culture of belonging where everyone can thrive. Learn more here: https://www.autodesk.com/company/global-belonging

Are you an existing contractor or consultant with Autodesk?

Please search for open jobs and apply internally (not on this external site).

Read the full bilingual original

Job Requisition ID #

26WD99904

L'affichage de poste en français suivra / The French job posting follows.

26WD99904, Machine Learning Developer 3D Geometry & Multimodal AI

Position Overview

We are seeking a Senior Machine Learning Developer to design, develop, and deploy machine learning solutions for next-generation AI-powered capabilities within Autodesk products. The ideal candidate has expertise across the machine learning lifecycle, including research, experimentation, data pipelines, model training, evaluation, MLOps, and production deployment.

You will work closely with researchers and developers to develop innovative AI solutions involving 3D geometry, multimodal AI, and generative AI, helping transform research into production-ready product capabilities. You enjoy solving complex technical problems, exploring new ideas, and collaborating across teams to deliver scalable, high-quality machine learning solutions.

Responsibilities

Design, develop, and optimize machine learning models for AI-powered product features involving 3D geometry, multimodal AI, and generative AI
Build and maintain scalable data pipelines and machine learning workflows for data preparation, model training, evaluation, and inference
Work with complex datasets and representations for model training, evaluation, and analysis
Design and implement evaluation methodologies, benchmarks, and experiments to measure model performance, robustness, and quality
Collaborate with researchers and developers to transform experimental ideas into scalable, production-ready product capabilities
Build and maintain MLOps workflows supporting model versioning, deployment, monitoring, and continuous improvement
Analyze model performance, identify failure modes, and implement improvements to enhance model accuracy, reliability, and efficiency
Document and present technical designs, experimental results, and findings to collaborators and leadership

Minimum Qualifications

Bachelor’s or Master’s degree in Computer Science, Machine Learning, Engineering, or equivalent industry experience
5+ years of professional experience developing machine learning solutions
Experience working across the machine learning lifecycle, including research, data pipelines, model training, evaluation, MLOps, and production deployment
Experience working in research or experimental environments with evolving requirements
Proficiency with modern deep learning frameworks (e.g., PyTorch, Hugging Face)
Experience with computational geometry and 3D data (e.g., meshes, B-Rep models)
Experience working with complex data representations, including 2D and 3D geometry
Experience with version control, reproducibility, and deploying machine learning models
Hands-on experience using modern AI/GenAI tools to improve software development, experimentation, or machine learning workflows
Excellent written and verbal communication skills

Preferred Qualifications

Advanced degree (MSc/PhD) in Machine Learning, Computer Science, or a related field
Experience with LLMs or generative AI systems (e.g., fine-tuning, prompting, evaluation, or agentic workflows)
Experience scaling machine learning training and data pipelines (e.g., using Ray or similar frameworks)
Experience with cloud platforms and services (e.g., AWS, Azure, Google Cloud Platform)
Knowledge of the design, manufacturing, AEC, or media & entertainment industries
Experience with Autodesk or similar products (e.g., CAD, CAE, CAM)

26WD99904, Développeur en apprentissage automatique : géométrie 3D et IA multimodale

Aperçu du Poste

Nous recherchons un développeur senior en apprentissage automatique chargé de concevoir, développer et déployer des solutions d’apprentissage automatique pour les fonctionnalités de nouvelle génération basées sur l’IA au sein des produits Autodesk. Le candidat idéal possède une expertise couvrant l’ensemble du cycle de vie de l’apprentissage automatique, notamment la recherche, l’expérimentation, les pipelines de données, l’entraînement des modèles, l’évaluation, les MLOps et le déploiement en production.

Vous travaillerez en étroite collaboration avec des chercheurs et des développeurs afin de mettre au point des solutions d’IA innovantes impliquant la géométrie 3D, l’IA multimodale et l’IA générative, contribuant ainsi à transformer la recherche en fonctionnalités produit prêtes à être mises en production. Vous aimez résoudre des problèmes techniques complexes, explorer de nouvelles idées et collaborer avec différentes équipes pour fournir des solutions d’apprentissage automatique évolutives et de haute qualité.

Responsabilités

Concevoir, développer et optimiser des modèles d’apprentissage automatique pour des fonctionnalités de produits basées sur l’IA impliquant la géométrie 3D, l’IA multimodale et l’IA générative
Construire et maintenir des pipelines de données et des workflows d’apprentissage automatique évolutifs pour la préparation des données, l’entraînement des modèles, l’évaluation et l’inférence
Travailler avec des ensembles de données et des représentations complexes pour l’entraînement, l’évaluation et l’analyse des modèles
Concevoir et mettre en œuvre des méthodologies d’évaluation, des tests de performance et des expériences pour mesurer les performances, la robustesse et la qualité des modèles
Collaborer avec des chercheurs et des développeurs pour transformer des idées expérimentales en fonctionnalités de produit évolutives et prêtes pour la production
Mettre en place et maintenir des workflows MLOps prenant en charge la gestion des versions, le déploiement, la surveillance et l’amélioration continue des modèles
Analyser les performances des modèles, identifier les modes de défaillance et mettre en œuvre des améliorations pour renforcer la précision, la fiabilité et l’efficacité des modèles
Documenter et présenter les conceptions techniques, les résultats expérimentaux et les conclusions aux collaborateurs et à la direction

Qualifications Minimales

Licence ou master en informatique, apprentissage automatique, ingénierie, ou expérience professionnelle équivalente
Au moins 5 ans d’expérience professionnelle dans le développement de solutions d’apprentissage automatique
Expérience couvrant l’ensemble du cycle de vie de l’apprentissage automatique, y compris la recherche, les pipelines de données, l’entraînement des modèles, l’évaluation, les MLOps et le déploiement en production
Expérience de travail dans des environnements de recherche ou expérimentaux aux exigences en constante évolution
Maîtrise des frameworks modernes d’apprentissage profond (par exemple, PyTorch, Hugging Face)
Expérience en géométrie computationnelle et en données 3D (par exemple, maillages, modèles B-Rep)
Expérience de travail avec des représentations de données complexes, y compris la géométrie 2D et 3D
Expérience en gestion de versions, en reproductibilité et en déploiement de modèles d’apprentissage automatique
Expérience pratique de l’utilisation d’outils modernes d’IA/IA générative pour améliorer le développement logiciel, l’expérimentation ou les workflows d’apprentissage automatique
Excellentes compétences en communication écrite et orale

Qualifications Souhaitées

Diplôme de niveau master ou doctorat (MSc/PhD) en apprentissage automatique, en informatique ou dans un domaine connexe
Expérience avec les grands modèles de langage (LLM) ou les systèmes d’IA générative (par exemple : réglage fin, formulation de prompts, évaluation ou workflows agentiques)
Expérience dans la mise à l’échelle de l’entraînement en apprentissage automatique et des pipelines de données (par exemple, à l’aide de Ray ou de frameworks similaires)
Expérience des plateformes et services cloud (par exemple, AWS, Azure, Google Cloud Platform)
Connaissance des secteurs de la conception, de la fabrication, de l’architecture, de l’ingénierie et de la construction (AEC), ou des médias et du divertissement
Expérience avec Autodesk ou des produits similaires (par exemple, CAO, IAO, FAO)

Learn More

About Autodesk

Welcome to Autodesk! Amazing things are created every day with our software – from the greenest buildings and cleanest cars to the smartest factories and biggest hit movies. We help innovators turn their ideas into reality, transforming not only how things are made, but what can be made.

We take great pride in our culture here at Autodesk – it’s at the core of everything we do. Our culture guides the way we work and treat each other, informs how we connect with customers and partners, and defines how we show up in the world.

When you’re an Autodesker, you can do meaningful work that helps build a better world designed and made for all. Ready to shape the world and your future? Join us!

Salary transparency

Salary is one part of Autodesk’s competitive compensation package. For Canada based roles, we expect a starting base salary between $123,000 and $180,400. Offers are based on the candidate’s experience and geographic location, and may exceed this range. In addition to base salaries, our compensation package may include annual cash bonuses, commissions for sales roles, stock grants, and a comprehensive benefits package.

Belonging

We take pride in cultivating a culture of belonging where everyone can thrive. Learn more here: https://www.autodesk.com/company/global-belonging

Are you an existing contractor or consultant with Autodesk?

Please search for open jobs and apply internally (not on this external site).

Apply About employer

More about this job

Responsibilities

Design, develop, and optimize machine learning models and scalable data workflows for product capabilities involving 3D geometry, multimodal AI, and generative AI. Evaluate and improve model performance, build MLOps workflows for deployment and monitoring, and collaborate with researchers and developers to bring experimental ideas into production.

Requirements

Requires a bachelor’s or master’s degree in a relevant field or equivalent industry experience, at least five years of professional machine learning experience, and experience across the machine learning lifecycle. Candidates should be proficient with modern deep learning frameworks and have experience with computational geometry, 2D and 3D data, reproducibility, model deployment, and modern AI tools.

Skills

Machine Learning
Deep Learning
PyTorch
Hugging Face
Computational Geometry
3D Geometry
Multimodal AI
Generative AI
Data Pipelines
Model Training
Model Evaluation
MLOps
Production Deployment
Model Versioning
Cloud Platforms
Communication

Benefits

Annual Cash Bonuses
Sales Commissions
Stock Grants

Visa sponsorship

Not detected in the job text

Keywords

Machine Learning
Deep Learning
PyTorch
Hugging Face
Computational Geometry
3D Geometry
2D Geometry
Meshes
B-Rep Models
Multimodal AI
Generative AI
Large Language Models
Fine-Tuning
Prompting
Agentic Workflows
Data Pipelines
Model Training
Model Evaluation
Inference
MLOps
Model Versioning
Model Deployment
Model Monitoring
Reproducibility
Ray
AWS
Azure
Google Cloud Platform
CAD
AEC

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