Machine Learning Resident - Client: Sarcomere Dynamics (12 month term)

“If you are interested in the application of Machine Learning for object manipulation with robots, this is the right opportunity for you. Be a part of the team of research and machine learning scientists building AI algorithms from the ground up and get mentored by some of the best minds in AI during the process.”  

Abdul Wahab, Machine Learning Scientist, Amii

Description

About the Role

This is a paid residency that will be undertaken over a 12-month period with the potential to be hired by our client, Sarcomere Dynamics, afterwards (note: at the discretion of the client). The Resident will report to an Amii Scientist and regularly consult with the client team to share insights and engage in knowledge transfer activities. Successful candidates will be members of a cross-functional project team with backgrounds in ML research, project management, software engineering, and new product development. This is a rare opportunity to be mentored by world-class scientists and to develop something truly impactful.

About the Client

Sarcomere Dynamics is a Canadian physical AI company at the forefront of revolutionizing automation. By combining high-dexterity robotics with AI, Sarcomere is addressing global labor shortages and increasing safety across industries. Sarcomere’s mission is to develop synthetic labor solutions that replicate human dexterity and adaptability, making complex automation accessible and efficient.

About the Project

Sarcomere is partnering with Magna International to bring physical AI onto the manufacturing floor. Magna is evaluating which of their work cells and manual tasks can be automated across their facilities, and Sarcomere is leading the robotics and technical side of that effort, from assessing candidate tasks to building and deploying the systems that perform them. The dexterous robotic hands give Magna reach into work that conventional grippers and fixed automation can't handle. This role will sit at the center of making that work, developing the learning-based control and perception stack that turns a real production task into a robot that can do it reliably.

Required Skills / Expertise

Are you passionate about building great solutions? You’ll be presented with opportunities to both personally and professionally develop as you build your career. We’re looking for a talented and enthusiastic individual with a solid background in machine learning, specifically in robotics for object manipulation.

Key Responsibilities:

• Develop, evaluate and improve policies for robot manipulation tasks, with a focus on improving task reliability and cycle time in industrial assembly settings.
• Build sim-to-real learning pipelines that augment real-world robot demonstrations with synthetic data generated in simulation.
• Design and implement domain randomization and data augmentation strategies across visual, physical, sensor, object, and environmental parameters to improve policy robustness and transfer to physical hardware.
• Analyze the distribution gap between simulated and real-world demonstrations and develop methods for distribution alignment, dataset balancing, filtering, or representation alignment where appropriate.
• Train, fine-tune, and benchmark robot manipulation policies using real-only, simulation-only, and blended real/synthetic datasets.
• Work with imitation learning, behavioural cloning, reinforcement learning, and/or vision-language-action approaches as appropriate to the problem and available data.
• Work with multimodal robotics data including vision, tactile signals, proprioception, robot joint states, actions, and teleoperation trajectories.
• Collaborate with robotics and controls engineers to ensure learned policies integrate effectively with the broader robot stack.
• Deploy and evaluate trained policies on physical robotic hardware in a controlled setting, iterating between simulation, offline evaluation, and real-world testing.
• Develop reproducible training and evaluation pipelines, including experiment tracking, dataset versioning, model checkpoints, benchmark definitions, and quantitative reporting.
• Communicate research findings, experimental results, limitations, and recommendations to both technical collaborators and project stakeholders.
• Stay current with emerging approaches in robot learning, dexterous manipulation, imitation learning, synthetic data generation, foundation models for robotics, and sim-to-real transfer, and assess their relevance to the project.
• Engage in regular client meetings, contributing to presentations and reports on project progress.

Required Qualifications: 

• Completion of an MSc or PhD in Computer Science, Robotics, Electrical/Computer Engineering, Machine Learning, or a related field, with research or applied experience in robotics, reinforcement learning, computer vision, or machine learning.
• Strong understanding of modern machine learning fundamentals, including deep learning, optimization, model evaluation, generalization, and statistical analysis of experimental results.
• Proficient in developing and training, fine-tuning and evaluating machine learning and deep learning models in PyTorch, JAX, and/or TensorFlow.
• Experience working with continuous, real-world, noisy, multimodal and sequential datasets.
• Experience with Linux, Git version control, writing clean code and documentation. 
• A positive attitude towards learning and understanding a new applied domain.
• Must be legally eligible to work in Canada.

Preferred Qualifications:

• Hands-on research or development experience in either robot learning, robotic manipulation, grasping or dexterous manipulation.
• Experience with sim-to-real transfer, including domain randomization, dynamics randomization, sensor noise modelling, representation alignment, or other methods for reducing the simulation-to-reality gap.
• Experience with dexterous or high-degree-of-freedom robotic hands, multi-finger grasping, or manipulation systems where contact dynamics are important.
• Experience with robotics simulators and tools such as NVIDIA Isaac Sim / Isaac Lab, Omniverse/OpenUSD, Replicator, MuJoCo, PyBullet, Gazebo, ManiSkill, or similar environments.
• Experience with at least one area directly relevant to robot learning, such as imitation learning, reinforcement learning, visuomotor policy learning, learning from demonstrations, computer vision, or multimodal learning.
• Working knowledge of robotics fundamentals such as coordinate frames, forward/inverse kinematics, robot state and action representations, trajectory data, sensors, and feedback/control loops.
• Familiarity with synthetic data generation for robotics.
• Experience learning policies from teleoperated human demonstrations, including trajectory preprocessing, action representation and dataset curation.
• Experience with modern robot policy architectures such as transformer-based policies, vision-language-action models, or other multimodal foundation-model approaches for robotics.
• Familiarity with current robotics foundation models and ecosystems such as Gemini Robotics, NVIDIA GR00T, OpenVLA, π/Physical Intelligence-style models, or related approaches.
• Publication record in peer-reviewed academic conferences or relevant journals in machine learning.
• Experience with deploying machine learning models in production environments or strong software engineering (or MLE) skills is a plus.
• Any skills that are comparable to the above qualifications are also beneficial and will be considered in the application process.

Non-Technical Requirements: 

• Desire to take ownership of a problem and demonstrate leadership skills.
• Interdisciplinary team player enthusiastic about working together to achieve excellence.
• Capable of critical and independent thought.
• Able to communicate technical concepts clearly and advise on the application of machine intelligence.
• Intellectual curiosity and the desire to learn new things, techniques, and technologies.
• Candidate location in Alberta or British Columbia is preferred but not required for this role.

Why You Should Apply

Besides gaining industry experience, additional perks include:

• Work under the mentorship of an Amii Scientist for the duration of the project
• Participate in professional development activities
• Gain access to the Amii community and events
• Get paid for your work (a fair and equitable rate of pay will be negotiated at the time of offer)
• Build your professional network
• The opportunity for an ongoing machine learning role at the client’s organization at the end of the term (at the client’s discretion)

How to Apply

If this sounds like the opportunity you've been waiting for, please don’t wait for the closing of September 18, 2026, to apply - we’re excited to add a new member to the Amii team for this role, and the posting may come down sooner than the closing date if we find the right candidate before the posting closes! When sending your application, please send your resume and cover letter indicating why you think you'd be a fit for Amii. In your cover letter, please include one professional accomplishment you are most proud of and why.

Applicants must be legally eligible to work in Canada at the time of application.

Amii is an equal opportunity employer and values a diverse workforce. We encourage applications from all qualified individuals without regard to ethnicity, religion, gender identity, sexual orientation, age or disability. Accommodations for disability-related needs throughout the recruitment and selection process are available upon request. Any information provided by you for accommodations will be kept confidential and won’t be used in the selection process.