A research engineer in robotics typically turns research ideas into working systems, often spanning perception, control and learning, and needs strong coding plus a solid grounding in machine learning or robotics. A strong candidate shows evidence of building and testing real systems, reading and reproducing papers, and working closely with engineers to move prototypes toward reliable operation. At a company building automated picking and packing stations with vision policies and teleoperation, that means comfort with messy real world data and hardware in the loop.
How it fits the AI era
AI is central to this kind of work, since vision based policies and learning from human demonstrations are the core methods. The role shifts toward designing training data, evaluating policies, and finding where models fail in the real world. Human judgment stays essential for framing problems, judging safety and reliability, and deciding what is good enough to ship.
How it builds AI experience
The role offers hands on experience with robot learning, computer vision and teleoperation data, which are in demand across robotics. Building this background means working with real robot setups, collecting and labeling demonstration data, and running careful evaluations rather than only training models offline. Public projects or papers on manipulation or vision policies help show that experience.
Worth showing
- Robot learning projects
- Python and ML tooling
- Real robot experiments
This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.
About InLoop Robotics
Robots-as-a-service automated picking and packing stations powered by continuous vision policies and human-in-the-loop teleoperation, per the company.
#J-18808-Ljbffr
Research Engineer in München Arbeitgeber: present of AI
Adaptyv Bio ist ein hervorragender Arbeitgeber, der Ihnen die Möglichkeit bietet, eine Finanzabteilung von Grund auf neu aufzubauen und dabei innovative Technologien zu nutzen. Mit einem flexiblen hybriden Arbeitsmodell in Lausanne fördern wir eine Kultur des Vertrauens und der Zusammenarbeit, während wir gleichzeitig individuelle Wachstumschancen bieten, um Ihre Fähigkeiten in der Automatisierung und im Finanzmanagement weiterzuentwickeln. Hier haben Sie die Chance, an der Spitze der KI-gestützten Forschung zu arbeiten und einen echten Einfluss auf die Zukunft der Wissenschaft zu nehmen.
