We are seeking a highly skilled and motivated AI Researcher/ Machine Learning Engineer to join the AI Research team. In this role, you will help innovate and accelerate the deployment of cutting-edge machine learning techniques for driving and vehicle monitoring technologies
You will play a key role in developing real-time, time-series-based machine learning models and ensuring their successful integration into both motorsport and commercial automotive ecosystems.
Key Responsibilities
- Develop, evaluate, and optimize machine learning algorithms for real-time time series analysis
- Design and implement models leveraging modern deep learning approaches (e.g., transformer architectures)
- Build and maintain scalable, production-ready codebases
- Define and execute experimental protocols to validate models and research hypotheses
- Support testing, validation, and deployment in both motorsport and automotive environments
- Stay up to date with advancements in AI, particularly in foundation models and time series analysis
Your Profile
- MSc (minimum) or PhD in Computer Science, Physics, Engineering, Mathematics, or related field
- 3–5 years of relevant experience in machine learning or AI research
- Strong research mindset with a practical, hands-on approach to problem solving
- Proactive, adaptable, and able to manage multiple priorities in a fast-paced environment
- Team-oriented with strong communication skills
Required Expertise
- Solid foundation in machine learning and deep learning (including classical methods)
- Experience with time series modeling and real-time data processing
- Understanding of transformer architectures and modern deep learning techniques
- Familiarity with foundation models (vision and/or multimodal models preferred)
Nice to have:
- Experience deploying deep neural networks in production environments
- Knowledge of embedded systems and/or cloud-based ML pipelines
- Experience integrating and testing algorithms in real-world systems
Technical Skills
- Languages: Python (required), C++, MATLAB/Simulink (plus)
- Frameworks: PyTorch (experience with PyTorch Forecasting and TensorRT is a plus)
- Libraries: Pandas, Scikit-learn (SKTime, Darts are a plus)
- Tools & Platforms: Linux, Docker, Git, HPC environments (AWS, Azure preferred)
- Optional: Experience with embedded platforms (e.g., NVIDIA Jetson)