About the job
About Us
Who We’re Looking For
As a Senior Machine Learning Engineer within our Delivery team, you will be an accomplished problem solver and technical leader focused on making a significant impact. You are adept at comprehending intricate engineering concepts across various industries, spearheading technical initiatives, and collaborating closely with customers—often working alongside them on-site—to integrate cutting-edge AI models into practical tools.
You possess a proven track record of delivering machine learning systems end-to-end at scale: you design, build, and test reliable, scalable ML data pipelines; you are skilled in exploring and manipulating 3D point-cloud and mesh data for geometry-aware modeling; you choose the most suitable libraries, frameworks, and tools while making pragmatic product decisions that set the Delivery team up for success. Operating at the intersection of data science and software engineering, you convert R&D outputs into reusable libraries, tooling, and products.
With a minimum of 3 years of industry experience (post-Master's or PhD) in a commercial setting, you are prepared to not only execute but also mentor and lead others. Your enthusiasm for taking ownership of complex work streams and guiding teams to success is matched by your commitment to continuously enhancing the systems and solutions you work on to ensure they are practical, impactful, and responsive to our customers' evolving needs.
This Role
As a Senior Machine Learning Engineer, your collaboration with Data Scientists, Simulation Engineers, and clients will be crucial in understanding and defining the engineering and physics challenges we tackle. You will engage with customers and leverage your influence to drive decisions regarding reliable deployment with measurable outcomes.
Your responsibilities will include:
- Taking ownership of the deployment of machine learning models and engineering surrogates (e.g., deep learning on CAE/CFD/FEA data, time-series forecasting, anomaly detection, optimization & control) to customer production environments.
- Communicating results and trade-offs effectively to various stakeholders.

