AI/ML Engineering Intern – Behavioral Safety (m/f/d) at Synapticon

Schönaich, Baden-Württemberg, Germany -

Full Time

Start Date

Immediate

Expiry Date

10 Sep, 26

Salary

0.0

Posted On

12 Jun, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

PyTorch, Reinforcement Learning, ROS2, Foundation Model Fine-tuning, Linear Algebra, Optimization, Probability, Machine Learning, Robotics, Multimodal Models, Semantic Intent Recognition, Real-time Control

Industry

Automation Machinery Manufacturing

Description

WHAT WE ARE LOOKING FOR We're looking for a motivated AI/ML Engineering Intern (ideally 6 months, mandatory internship) to join our Safety AI team in Schönaich (close to Stuttgart), with a flexible start date. If you're excited about applying machine learning to genuinely open research questions in humanoid robotics — from semantic intent recognition to AI-based fall strategies — this is a rare opportunity to do research with a direct path to product. HERE'S WHERE YOU MAKE AN IMPACT Prototype multimodal foundation model integration for robot safety context — exploring how large-scale models can give robots semantic awareness of their environment and the people in it Develop and test AI-based fall strategy controllers, working on one of the more genuinely open research questions in humanoid deployment Implement ML model inference in the robot control loop — bridging the gap between research models and the timing constraints of real-time robot software Benchmark behavioral safety classifiers for semantic intent recognition in simulation, building the evidence base for what these models can and can't reliably distinguish in deployment Build evaluation frameworks for AI safety behaviour aligned with ISO/IEC TR 5469 — contributing to a principled, documented approach to assessing AI in safety-relevant contexts WHO YOU ARE Enrolled MSc student in Machine Learning, Computer Science, or Robotics — with a research mindset and the discipline to support it with rigorous, reproducible evaluation Strong PyTorch skills; hands-on RL experience using Stable Baselines3, Isaac Lab, or similar frameworks — you've trained policies and analysed why they fail, not just run examples You've integrated ML inference pipelines with ROS2 — or are confident you can make it work cleanly in a real-time control context Experience with foundation model fine-tuning is a strong plus — this work involves adapting large models for a specific purpose, not just deploying them off the shelf You're interested in safety-critical AI — not as a compliance checkbox, but as a genuinely hard technical and methodological problem Strong mathematical foundation across linear algebra, optimisation, and probability — you're comfortable with the theory behind what you implement WHAT'S IN IT FOR YOU Flexible working hours when life takes unexpected turns Company support for gym memberships through Wellpass Birthday vouchers Drinks and coffee (free of charge, of course) Snacks and fruit for all employees, as well as breakfast every Monday Regular global team-building events such as boot camps, skiing, and summer BBQs An international team with long-term prospects Highly competent colleagues who are experts in their field and happy to support you in your learning and growth Modern office space Our company's WG (upon availability) About us HACKING THE PHYSICS AND ECONOMICS OF MOTION CONTROL WITH DIGITAL TECHNOLOGY At Synapticon, we are revolutionizing motion control for robotics and machines—right at the point of motion. Our approach is completely new: we integrate previously separate components such as motors, drives, sensors, and gearboxes into seamless units. At the same time, we are digitizing performance and quality factors that previously depended on expensive mechanical manufacturing. World-leading innovators are choosing Synapticon to pave their way into the future of intelligent, motion-controlled systems. Be part of our success story – apply now at Synapticon!

Responsibilities

The intern will prototype multimodal foundation models for robot safety and develop AI-based fall strategy controllers for humanoid robots. They will also implement ML inference in real-time control loops and build evaluation frameworks aligned with ISO/IEC TR 5469.