以上为北京时间。链接检查不代表仍在招聘，申请前请以招聘官网为准。
按这个岗位的城市和方向提醒，确认后才开启，可选择每日或每周。
负责多模态基础模型接入机器人安全场景，开发并测试AI防跌倒控制策略与模型推理。还将评估语义意图分类器，并建设符合ISO/IEC TR 5469的AI安全行为评测框架。
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
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
Synapticon公司招聘电气工程实习生，参与人形机器人原型开发，负责线束设计、连接器接口文档、电源分配设计及EMC屏蔽审查，实习期约6个月，团队国际化，办公地点位于斯图加特附近。
