AI/ML Engineer (m/f/d) Autonomous Systems

Job description

We are MOTOR Ai . Our mission is to make Level 4 autonomous driving a reality in Germany and beyond. By combining state-of-the-art AI with the expertise of our multidisciplinary team, we are poised to introduce the first solution of its kind in Germany that is eligible for certification on European roads. Since 2017, we have been steadfastly pursuing a single goal: to shape a future that is already in motion. Tasks Model Development & Training - Design, train, and fine-tune machine learning models to meet performance, accuracy, and robustness requirements for autonomous driving use cases Model Deployment & Integration - Integrate trained models into production systems, ensuring reliable, efficient, and safe operation within the overall software stack Experimentation & Evaluation - Run structured experiments and evaluations to validate model performance, using established metrics and benchmarks Performance Optimization - Optimize models for latency, memory, and compute constraints relevant to real-time autonomous systems Cross-Functional Collaboration - Partner with data, perception, and simulation teams to ensure models are built on the right data and validated against real-world scenarios Requirements A completed degree in computer science, machine learning, data science, or a comparable field - or equivalent vocational training/apprenticeship in a relevant technical discipline. Hands-on experience counts at least as much as the formal qualification 3+ years of relevant hands-on experience in applied AI/ML engineering Strong knowledge of deep learning and reinforcement learning; familiarity with Active Inference approaches is a plus. Experience in the automotive industry and a track record of turning research papers into working product features are both an advantage Strong Python skills with solid ML fundamentals, and hands-on experience with deep learning frameworks (PyTorch and/or TensorFlow) Experience with reinforcement learning tooling (e.g. Stable-Baselines3, RLlib) and experiment-tracking tools (e.g. MLflow, Weights & Biases) Experience deploying and optimizing models for production/real-time systems (e.g. ONNX, TensorRT, C++ integration) Solid engineering practices: Git, CI/CD pipelines, and containerization (Docker, ideally Kubernetes) Comfortable working with cloud/GPU compute environments (AWS, GCP, or Azure) and data tooling (NumPy, Pandas, SQ…