Senior Machine Learning Engineer

We’re looking for a Senior ML Engineer (Computer Vision & Perception) to build the in-cabin Occupant Monitoring System (OMS) for our robotaxis from the ground up. As our first hire in this area, you will shape how our vehicles understand what happens inside the cabin: who is on board, whether seatbelts are fastened, whether anything was left behind, and whether passengers behave safely. You will define the approach and take the system from first prototype to real-time inference on in-vehicle hardware, working alongside experienced engineers across the company.

You will be based on site, with regular hands-on work in our vehicles, collaborating closely with our engineering teams in an agile setup. As this is a new area for us, you will help select the frameworks, tooling, and in-vehicle compute for our perception stack, with a focus on real-time performance and reliability for safety-critical functions. Day to day, we use Azure, Jira, and Microsoft Teams, and you’re free to choose your own development tools.

Who are we?

At Verne, we're building an ecosystem to progress our society through a journey of curiosity and innovation. Named in honor of Jules Verne "the man who invented the future,". We are the first European solution for autonomous journeys. We adopt an integrated approach, including vehicle design, infrastructure development, and the full digital experience.

As a growing startup, we thrive on curiosity, innovation, and bold ideas. If you’re ready to make your mark in a fast-paced, dynamic environment, this could be the role for you.

What will you do?

As an Senior ML Engineer, your responsibilities will include:

• Own the OMS perception system end to end and define its technical approach, tooling, and roadmap, from research and prototyping through to in-vehicle deployment

• Design, train, and fine-tune models for in-cabin object detection, pose estimation, and action recognition (e.g. occupancy, child presence, seatbelt use, left-behind objects, smoking, vandalism)

• Set up in-cabin data collection with the relevant teams, including camera selection and placement, and build pipelines for collecting, labelling, and curating RGB and infrared data, including staged edge cases

• Define evaluation metrics and test sets, and validate performance across lighting conditions, occupant types, and vehicle interiors

• Optimise and deploy models for real-time inference on embedded automotive hardware (e.g. quantisation, pruning, TensorRT)

• Combine model outputs with rule-based logic to detect safety events and rule violations with low false-alarm rates

• Apply privacy-by-design and secure-by-design principles to data handling and on-device processing, in line with GDPR and cybersecurity best practices