Machine Learning Engineer

We are looking for a Senior Data Scientist / Machine Learning Engineer to join our Technology and Digital function within the SW & Analytics Lab team.

The candidate will play a key role in bridging advanced data science with software engineering, contributing both to algorithm development and end-to-end ML system implementation. In coordination with the Tech Lead, they will be responsible for analyzing project requirements, conducting feasibility studies, and leading the design, development, and deployment of scalable ML and computer vision solutions.

The role includes defining technical guidelines, software architectures, and development standards, as well as implementing best practices in DevOps, CI/CD, and MLOps to ensure reliability and reproducibility. The candidate will coordinate a team of data scientists and engineers, driving activities from data processing and model training to integration in production environments—both on-board (vehicle) and off-board (cloud or infrastructure).

The candidate will collaborate with cross-functional teams and business stakeholders to align technical solutions with project goals, while fostering innovation in the adoption of cloud-based AI and ML platforms.

Essential responsibilities:

• Analyze project requirements, perform feasibility studies, and define time and cost estimations in coordination with the ML & CV Team Leader
• Define the project’s technical guidelines, architecture, and development standards, overseeing all phases from concept to deployment.
• Design and maintain detailed development and validation plans following Agile (SAFe) methodology, ensuring proper backlog prioritization and coordination with other teams.
• Coordinate a team of up to 5 people and actively contribute to development activities as a senior team member.
• Oversee the full ML lifecycle: data processing, model training, evaluation, deployment, and monitoring in production environments.
• Support integration of algorithms both on-board (vehicle) and off-board (infrastructure/cloud), ensuring robustness and maintainability.
• Collaborate closely with Product Owners and Software Engineering teams on project execution, tracking, and reporting.
• Promote best practices in version control, CI/CD, testing, and MLOps to ensure reproducibility and scalability of ML solutions.