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Vector Institute

Applied ML Scientist, Physical AI

Toronto, ON, Canada

Pay: $125,800 to $157,300 CAD / year
Job type: Full-time
Posting language: English
Posted: Sep 29, 2026

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Original job description

Applied Machine Learning Scientist - Physical AI

POSITION SUMMARY

As an Applied Machine Learning Scientist, Physical AI, you will develop and apply state-of-the-art machine learning methods to enable intelligent, reliable, and safe robots to perceive, reason, and act in the physical world. You will focus on robotic manipulation and embodied AI, bridging advances in machine learning with practical robotic systems.

You will work with ML researchers, Applied ML specialists, research professionals, and robotics experts to design, develop, evaluate, and deploy robotic AI policies in simulation and on physical embodiments. This scope includes working across data generation, simulation environments, model/policy development and evaluation, with a focus on bridging the sim-to-real gap and enabling reliable behavior in real-world environments.

The role encourages publishing applied research while maintaining a strong focus on translating research advances into impactful prototypes, tools, and real-world solutions.

KEY RESPONSIBILITIES

Research and implement state-of-the-art methods for robotic manipulation, robot learning, and Physical AI;
Design, develop, and benchmark robotic AI policies using modern architectures like vision-language-action models, world models, diffusion policies, etc.;
Develop synthetic data generation, simulation, and evaluation strategies for robotic learning, including the research and application of world models, with a focus on reducing the sim-to-real gap and identifying and mitigating model failure modes;
Design policy training and fine-tuning pipelines that combine teleoperation data and synthetic demonstrations with techniques like reinforcement learning and imitation learning for dexterous manipulation;
Investigate approaches for improving the generalization, robustness, sample efficiency, safety, and adaptability of learned robotic policies across tasks, environments, and hardware platforms;
Collaborate with researchers, AI engineering team members, and external partners to build prototypes, demonstrations, and MVPs;
Work with engineering teams and external collaborators to build tools and frameworks that accelerate the development and deployment of robotic policies on physical embodiments;
Provide scientific guidance on Physical AI, robotics, and robotic manipulation strategies, technical approaches, and research roadmaps;
Serve as a technical lead for Vector Physical AI and robotics projects, including contributing to and/or leading peer-reviewed publications and technical reports;
Share expertise through training, workshops, and knowledge-transfer initiatives; and,
Other related duties as assigned from time to time.

KEY SUCCESS MEASURES

Successfully deliver applied research collaborations and high-quality implementations, bridging Physical AI research with real-world deployment and creating recognized impact for Vector and its partners;
Tools and MVPs developed in collaboration with the AI engineering team achieve strong adoption and demonstrate extensibility, usability, and practical utility;
Establish a reputation amongst Vector researchers as a key contributor in translating advances in ML and robotics research into new tools, working prototypes, and real-world applications;
Research contributions result in publications at leading venues such as NeurIPS, ICML, ICLR, ICRA, CVPR, CoRL, IROS, and RSS;
The role provides technical leadership on emerging approaches in Physical AI and embodied AI within Vector; and,
Organizational capacity in Physical AI is strengthened by enabling researchers, engineering team members, and partners to understand and apply emerging robotic AI technologies.

PROFILE OF THE IDEAL CANDIDATE

PhD in computer science, computer engineering, robotics, electrical engineering, mechanical engineering, or a related field, with a research focus on machine learning or robotics preferred;
Experience developing and evaluating ML policies for robots, ideally including hands-on experience deploying policies on physical robotic hardware;
Experience with robotic platforms, sensors, simulation environments, and/or robotics software such as ROS/ROS2;
Strong Python skills and experience with deep learning frameworks such as PyTorch or JAX and related libraries for Generative AI and Physical AI development;
Strong understanding of machine learning and Physical AI fundamentals, including deep learning, representation learning, reinforcement learning, diffusion, vision language and reasoning models, and model evaluation;
Strong research record demonstrated through publications, open-source contributions, or impactful robotics/ML projects;
Experience with NVIDIA's Physical AI ecosystem (or comparable platforms), including Isaac Lab, Isaac Sim, and Cosmos, is considered an asset;
Experience with vision-language-action models, world models, multimodal foundation models, or robotics foundation models is considered an asset;
Experience optimizing and deploying machine learning models on real-world robotic systems, including considerations such as latency, compute constraints, reliability, and safety, is considered an asset;
Knowledge or experience in AI safety, interpretability, transparency, robustness, or responsible AI, particularly as applied to embodied or robotic systems, is considered an asset; and,
Experience building agentic AI systems for multi-step workflows including failure recovery and foundation model reasoning is considered an asset.

TOTAL REWARDS: The expected salary for this position will be $125,800 - $157,300 per year, plus benefits if applicable. The final salary offer will reflect the successful candidate's experience, skills, and qualifications, in alignment with the Vector Institute's Compensation Policy and may differ from above.

The Vector Institute’s Total Rewards approach extends beyond traditional compensation and benefits. Full-time employees are eligible for a comprehensive suite of supports that recognize and value employees, including vacation time, floater days, GRRSP, a Health Spending Account, a Summer Hours program, and flexible work arrangements.

POSITION STATUS: This posting is for a new vacancy.

INCLUSION AND EQUAL OPPORTUNITY EMPLOYMENT: Vector believes AI powers possibility by advancing cutting-edge research and translating it into real-world impact through collaboration with research, industry, and government. Vector is committed to fostering a diverse and inclusive culture that reflects its values.

The Vector Institute welcomes applications from all qualified candidates, including those who are Indigenous, 2SLGBTQIA+, racialized persons/visible minorities, women, and people with disabilities.

If you require an accommodation at any stage of the recruitment or selection process, please contact hr@vectorinstitute.ai. The Vector Institute team will be happy to work with you to ensure your experience is as inclusive and accessible as possible.

JOIN OUR COMMUNITY: Check out the Vector Institute’s Careers Page to explore open opportunities at Vector and Follow Vector on X, LinkedIn, and Bluesky to stay connected with the latest developments in Ontario's AI ecosystem and the Vector Institute.

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More about this job

Responsibilities

The Applied ML Scientist will develop and apply state-of-the-art machine learning methods to enable intelligent and safe robots to operate in the physical world. They will collaborate with researchers and engineers to design, evaluate, and deploy robotic AI policies in both simulation and physical environments.

Requirements

Candidates must hold a PhD in a relevant field such as computer science, robotics, or engineering with a strong research focus on machine learning. Proven experience in developing and deploying ML policies on physical robotic hardware and proficiency in deep learning frameworks are essential.

Skills

Machine learning
Robotics
Physical AI
Python
PyTorch
JAX
Reinforcement learning
Imitation learning
Computer vision
Diffusion models
Robotic manipulation
Simulation
ROS2
Embodied AI
Synthetic data generation

Benefits

Vacation time
Floater days
GRRSP
Health Spending Account
Summer Hours program
Flexible work arrangements

Visa sponsorship

Not detected in the job text

Keywords

Machine Learning
Robotics
Physical AI
Embodied AI
Python
PyTorch
JAX
Reinforcement Learning
Imitation Learning
Diffusion Policies
Vision-Language-Action Models
World Models
ROS2
Isaac Lab
Isaac Sim
Sim-to-real
Dexterous Manipulation
Generative AI
Representation Learning
Robotic Manipulation
AI Safety
Model Evaluation
Foundation Models
Computer Science
Electrical Engineering
Mechanical Engineering
Technical Leadership
Applied Research
Prototyping
Data Generation

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