Vision Engineer - Learning-Based Robotic Manipulation (f/m/d)
Pubblicato il 07-09-2026 - Raicam in Piemonte
pAICAM Mondovi Site (Cuneo) is looking for a /ppstrongVision Engineer - Learning-Based Robotic Manipulation /strong /ppbr / /ppstrongAbout AICAM /strong /ppAICAM ( is Raicam#39;s robotics division, focused on developing and industrializing service robots for real hospitality environments. /ppbr / /ppstrongAbout the role /strong /ppWe are looking for a Vision Engineer - Learning-Based Robotic Manipulation (f/m/d) to help develop the learning-based manipulation capabilities of our robots, from perception to policy to execution on real hardware. You will work on the pipeline that turns multi-camera 3D perception into activity reasoning and robust bimanual manipulation, closing the loop by iteratively deploying on the real robot. /ppYou will join our robotics team and work closely with the perception, control, and hardware engineers who own the surrounding stack, focusing on the computer vision and robot learning core that enables our robots to carry out manipulation tasks in dynamic, unstructured hospitality environments such as restaurants, hotels, and hospitals. /ppbr / /ppstrongResponsibilities /strong /pulliOwn the teleoperation and demonstration data pipeline: the collection setup, data quality and curation, and the path from recorded episodes to trained policies. /liliBuild high-fidelity simulation of the robot for training manipulation policies and rapid testing, with a strong focus on domain randomization and sim-to-real transfer. /liliDesign and train learning-based policies for bimanual manipulation in hospitality settings, and bring them from simulation to deployment on real hardware. /liliDevelop the 3D perception that drives manipulation, including multi-view depth and point-cloud processing, 6-DoF pose and grasp estimation, and scene representations built from RGB-D.
/liliDevelop grasp strategies for the objects our robots encounter in service environments, including rigid and deformable items handled from imperfect real-world perception. /liliValidate on real hardware through iterative cycles of refinement and redeployment, working with the perception and control engineers to integrate the manipulation stack. /li /ulpbr / /ppstrongRequirements /strong /pulliStrong background in learning-based robotic manipulation, with hands-on experience on real hardware. /liliSolid computer vision and 3D learning: point clouds, depth, multi-view geometry, and 6-DoF pose estimation or grasp regression. /liliHands-on experience with robotic grasping and/or imitation learning. /liliExperience with teleoperation and data collection for robot learning. /liliProficiency in Python and PyTorch, with ROS 2 familiarity. /liliWorking experience with at least one robotics simulator (Isaac Sim / Lab, MuJoCo, ManiSkill). /liliProven ability to debug and tune robotic systems in real-world conditions, dealing with sensor noise, latency, and mechanical limitations. /li /ulpbr / /ppstrongNice to have /strong /ppExperience in any of the following is a plus. We do not expect one person to cover all of them: /pulliVision-Language-Action (VLA) models: fine-tuning or deploying policies such as π0 / π0.5, OpenVLA, SmolVLA, ACT, or Diffusion Policy.
/liliReinforcement learning for manipulation, with sim-to-real transfer. /liliGPU-accelerated simulation environments (Isaac Lab, ManiSkill, MuJoCo MJX). /liliAdvanced 3D scene representations for manipulation (Scene Graphs, NeRF, 3D Gaussian Splatting). /liliHands-on experience with robot embodiments such as mobile manipulators, dual-arm systems, or humanoids. /li /ulpbr / /ppstrongWhat we are looking for /strong /pulliEngineers who bridge theory and real-world execution, comfortable both reading a research paper and diagnosing why a policy fails on the real robot. /liliA focus on robustness and generalization over ideal-case performance or cherry-picked demos. /liliA strong ownership mindset on complex subsystems, with the initiative to push back when something is not working. /liliGenuine interest in solving open perception and manipulation problems /li /ulpbr / /ppstrongCompensation Classification /strong /ppbr / /pulliBargaining Agreement: Italian National Collective Bargaining Agreement (CCNL Metalmeccanico Industria). /liliClassification Target: Expected job level B2, corresponding to a statutory base minimum of € 33058,74 gross per year. /liliTotal Rewards Package: Final financial offers will clearly exceed collective bargaining minimums to reflect individual expertise and prior achievement. The complete rewards proposal will include an individual merit allowance on top of the base minimum, a fixed production allowance, welfare benefits, performance bonuses, healthcare coverage (Metasalute), and pension options (Cometa). /li /ulpbr / /ppemWe are an Equal Opportunity Employer. Applications are welcome from all individuals regardless of gender, age, sexual orientation, ethnicity, background, or disability status. /em /ppbr / /p