2 months ago
Berlin, GermanyMid Level
Responsibilities
- Design, train, and fine-tune machine learning models for autonomous driving use cases.
- Integrate trained models into production systems for reliable, efficient, and safe operation.
- Run structured experiments and evaluations using established metrics and benchmarks.
- Optimize models for latency, memory, and compute constraints in real-time autonomous systems.
- Collaborate with data, perception, and simulation teams to ensure appropriate data and real-world validation.
Requirements
- Completed degree in computer science, machine learning, data science, or a comparable field, or equivalent vocational training in a relevant technical discipline.
- At least 3 years of relevant hands-on experience in applied AI/ML engineering.
- Strong knowledge of deep learning and reinforcement learning; Active Inference familiarity is a plus.
- Strong Python skills, solid machine learning fundamentals, and hands-on experience with PyTorch and/or TensorFlow.
- Experience with reinforcement learning tooling such as Stable-Baselines3 or RLlib and experiment-tracking tools such as MLflow or Weights & Biases.
- Experience deploying and optimizing models for production or real-time systems, including ONNX, TensorRT, or C++ integration.
- Experience with Git, containerization, cloud/GPU compute environments, and data tooling including NumPy, Pandas, and SQL.
- Kubernetes, automotive industry experience, research-to-product experience, CARLA, and ROS/ROS2 familiarity are advantageous.
- Valid driving license and English proficiency at C1 level are required; German is a plus.
- Open-minded and curious approach to trying new ideas and approaches.
Benefits
- Costs of further education and training are covered.
- Flexible working hours accommodating personal responsibilities.
- Corporate Benefits partner discounts.
- Subsidy for public transport tickets.
- Attractive location in Wedding with strong public transport connections.
- Face-to-face collaboration in a non-fully-remote work environment.
- Work on socially relevant autonomous-driving technology in a diverse international team.
Tech Stack
Categories
ML EngineeringRobotics
