The posting
About the Company
Haply Robotics builds the world's most precise haptic devices. Our products (Inverse3, VerseGrip, MinVerse, Haply Hub, HARP) are deployed across research labs, industrial integration projects, and production environments where the difference between "good" and "right" is measured in milliseconds and millinewtons. As robots and autonomous systems become more capable, humans still need intuitive ways to teach, guide, and supervise physical tasks. Haply builds the platform that allows people to program and train machines through touch and motion.
Current Research Themes
- Our Physical AI team is focused on developing the interface layer for Physical AI—connecting humans, foundation models, simulation, and physical robotic systems.
- Human-in-the-Loop Physical AI
- Foundation Models and Robot Learning
- Simulation and Sim2Real
- Haptics and Teleoperation
Our teleoperation systems enable humans to interact naturally with simulated and physical robots while simultaneously generating rich multimodal datasets containing motion, robot state, human input, visual information, and force/haptic signals.
Responsibilities
- As a member of Haply's Physical AI team, you will work directly with researchers and engineers on emerging problems at the intersection of robotics, machine learning, simulation, and haptics.
- Depending on your background and the research problem, your work may include:
- Building and configuring simulated robotic environments for training, data generation, and evaluation.
- Developing end-to-end robot-learning and Physical AI pipelines.
- Working with and fine-tuning foundation models for robotic applications.
- Implementing and evaluating methods from the latest robot-learning and Physical AI research literature.
- Developing and training imitation-learning, reinforcement-learning, diffusion-based, and other learned robotic policies.
- Developing human-in-the-loop learning frameworks that allow operators to demonstrate, guide, intervene in, or correct robotic policies during training and deployment.
- Collecting demonstrations and interaction data through haptic teleoperation.
- Building pipelines for collecting, synchronizing, processing, and training on multimodal robotic datasets, including vision, robot state, actions, force/torque, haptic signals, and human input.
- Investigating how force and haptic information can be incorporated into multimodal foundation models and learned robotic policies.
- Deploying and evaluating learned policies on physical robotic systems.
- Designing experiments to understand and reduce the Sim2Real gap.
- Evaluating and implementing new methods emerging from the rapidly developing Physical AI and embodied AI research ecosystem.
- The role is inherently hands-on. You should be comfortable moving between machine-learning models, Python and C++ code, GPU training pipelines, simulation environments, experimental datasets, and physical robotic systems.
Must Have
- Familiarity with Physical AI and embodied AI workflows, including developing, training, and evaluating robotic systems across simulated and real-world environments.
- Experience with the NVIDIA robotics and simulation ecosystem, including creating and configuring simulated scenes and robotic environments.
- Familiarity with foundation models for robotics, including working with pretrained models, fine-tuning models for downstream tasks, and integrating them into robotic learning workflows.
- Practical experience with modern deep-learning frameworks, particularly PyTorch, including model training, evaluation, and inference.
- Experience working with robotics and machine-learning datasets, including data collection, preprocessing, dataset management, and training pipelines.
- Experience with modern machine-learning experimentation workflows, including tools such as Weights & Biases (W&B) or MLflow for experiment tracking, visualization, model evaluation, and management of training runs.
- Proficiency in Python and working knowledge of C++, particularly for robotics, simulation, or machine-learning applications.
- Experience developing in a Linux environment and familiarity with modern collaborative software-development practices, including Git, debugging, testing, and version control.
- An intermediate background in robotics, including an understanding of fundamental concepts such as kinematics, dynamics, coordinate frames, robotic control, and motion planning.
- Hands-on experience with robotic hardware, including setting up and configuring robotic systems, operating robots safely, interfacing with sensors and actuators, and troubleshooting hardware/software integration.
- Experience working across the simulation-to-hardware workflow, including deploying and evaluating robotic behaviors or learned policies on physical systems.
- The ability to read, understand, and implement methods from contemporary robotics and machine-learning research, and to rapidly evaluate and integrate new approaches as the Physical AI ecosystem evolves.
- A Bachelor's, Master's, or PhD in robotics, computer science, electrical/computer engineering, mechanical engineering, machine learning, or a related field—or equivalent practical experience—is preferred.
Nice to have
- Experience working with multimodal and temporal robotic data, including the collection, synchronization, processing, and representation of signals such as vision, robot state, actions, force/torque, and haptic information.
- Experience with robot and sensor calibration, coordinate transformations, camera calibration, force/torque sensing, encoders, or other robotic sensing systems.
- Experience with real-time robotic systems, including considerations around control-loop frequency, communication latency, synchronization, and deterministic or low-latency software.
- Experience with AWS or other cloud computing platforms, particularly for machine-learning training, data storage, experiment management, or distributed computational workloads.
- Experience deploying or training large machine-learning models using GPU-based computing infrastructure.
- Experience with ROS/ROS 2 or similar robotic middleware.
- Experience with robotic manipulators, teleoperation systems, or contact-rich manipulation.
- Experience collecting and managing robot demonstration datasets, particularly datasets generated through human demonstrations or teleoperation.
- Experience with Sim2Real techniques, such as domain randomization, system identification, dynamics randomization, or adaptation methods.
- Experience developing or working with haptic interfaces, force-feedback systems, or bilateral teleoperation.
The Opportunity
Physical AI is rapidly changing how intelligent systems interact with the physical world. At Haply, we believe that touch and physical interaction are fundamental components of that future.
This role provides an opportunity to work across the full Physical AI stack—from simulation and foundation models to human demonstrations, multimodal datasets, robotic control, and deployment on physical hardware.
Our goal is to develop technologies that allow humans, intelligent models, and physical robots to interact more naturally, while exploring how high-fidelity touch and force information can become fundamental components of the emerging Physical AI ecosystem
What We Offer
Each employee has a chance to see the impact of his work. You can make a real contribution to the success of the company.
Several activities are often organized all over the year, such as weekly sports sessions, team building events, monthly drink, and much more
Perks
A full-time position Attractive salary package.
Trainings
12 days / year, including 6 of your choice.
Sport Activity
Play any sport with colleagues, the bill is covered.
Eat & Drink
Fruit, coffee and snacks provided.
