Robot Learning Engineer – Physical AI

Robot Learning Engineer – Physical AI

Tasq.ai operates at the intersection of AI, data, and human intelligence, helping leading companies build, train, evaluate, and improve advanced AI systems.

We are looking for a hands-on Robot Learning Engineer to lead our Physical AI models training domain.

This is a high-impact role combining deep technical expertise with ownership and execution. You will help shape our approach to training and evaluating models for real-world and embodied AI applications, working across data, model development, experimentation, and evaluation.

You will work closely with our technology and product leadership and have significant influence over how we build and scale our Physical AI capabilities.

What You'll Do

• Lead the technical development of Tasq’s Physical AI and robot-learning model training capabilities.
• Design and execute model training, fine-tuning, and evaluation workflows.
• Work with multimodal and real-world data, including vision, video, sensor and interaction data.
• Develop and improve data and training pipelines for robotics and embodied AI use cases.
• Design experiments and evaluation methodologies to understand and improve model performance.
• Explore and implement state-of-the-art approaches in robot learning, imitation learning, reinforcement learning and related areas.
• Collaborate with internal teams and external partners on Physical AI projects.
• Translate research and emerging technologies into practical, scalable solutions.
• Help define the technical roadmap and best practices for this growing area at Tasq.

What We're Looking For

• Strong background in machine learning, deep learning, robotics, computer vision or a related field.
• Familiarity with multimodal models and/or models operating in physical environments.
• Understanding of robot learning concepts such as imitation learning, reinforcement learning, behavior cloning or policy learning.
• Ability to work independently and take ownership of ambiguous technical challenges.
• Comfortable operating in a fast-moving environment where the technology and requirements continue to evolve.

Nice to Have

• Experience with robotics, autonomous systems, embodied AI or Physical AI.
• Experience working with large-scale video, sensor or robotics datasets.
• Experience with Vision-Language-Action (VLA) models or related multimodal architectures.
• Experience taking research concepts into production or real-world deployments.
• Previous experience building a new technical capability or domain from the ground up.