Machine Learning Engineer

About us PhysicsX is a deep-tech company with roots in numerical physics and Formula One, dedicated to accelerating hardware innovation at the speed of software. We are building an AI-driven simulation software stack for engineering and manufacturing across advanced industries. By enabling high-fidelity, multi-physics simulation through AI inference across the entire engineering lifecycle, PhysicsX unlocks new levels of optimization and automation in design, manufacturing, and operations — empowering engineers to push the boundaries of possibility. Our customers include leading innovators in Aerospace & Defense, Materials, Energy, Semiconductors, and Automotive. Note:  We are currently recruiting for multiple positions, however please only apply for the role that best aligns with your skillset and career goals. Who We're Looking For As a Machine Learning Engineer in Delivery, you are a problem solver who stays anchored to impact. You are someone who can grasp advanced engineering concepts across multiple industries, and excel at working directly with customers (and often side-by-side with them on-site) to embed cutting-edge AI models into tools that are useful and used. You’ve shipped ML systems end-to-end and at scale: you design, build and test reliable, scalable ML data pipelines; you know how to explore and manipulate 3D point-cloud and mesh data to enable geometry-aware modelling; you select the right libraries, frameworks and tools. Working at the intersection of data science and software engineering, you translate R&D and project outputs into reusable libraries, tooling and products. With at least 2 years industry experience (post Masters or PhD) in a commercial, non-research environment. You're truly excited about taking ownership of complex work streams and guiding teams to success, while continuously improving the systems and solutions you work on to ensure they are practical, impactful and meet the evolving needs of our customers. Note:  Due to the nature of our aerospace and defense work, this position is open to US citizens only. This Role  As a Machine Learning Engineer, you'll work closely with our Data Scientists, Simulation Engineers, and customers to understand and define the engineering and physics challenges we are solving. You will iterate with customers and use your influence to drive decisions around reliable deployment with measurable outcomes. What you will do Work closely with our simulation engineers, data scientists and customers to develop an understanding of the physics and engineering challenges we are solving Design, build and test data pipelines for machine learning that are reliable, scalable and easily deployable Explore and manipulate 3D point cloud & mesh data Own the delivery of technical workstreams Create analytics environments and resources in the cloud or on premise, spanning data engineering and science Identify the best libraries, frameworks and tools for a given task, make product design decisions to set us up for success Work at the intersection of data science and software engineering to translate the results of our R&D and projects into re-usable libraries, tooling and products Continuously apply and improve engineering best practices and standards and coach your colleagues in their adoption You'll also have the opportunity to travel to customer sites in North America, Europe, Asia, Oceania, for an average of 3-4 weeks per quarter , where you'll collaborate closely with customers to build solutions on-site.   What you bring to the table Experience applying Machine learning methods (including 3D graph/point cloud deep learning methods) to real-world engineering applications, with a focus on driving measurable impact in industry settings. Experience in ML/Computational statistics/Modelling use-cases in industrial settings (for example supply chain optimisation or manufacturing processes) is encouraged. A track record of scoping and delivering projects in a customer facing role 2+ years’ experience in a data-driven role, with exposure to software engineering concepts and best practices (e.g., versioning, testing, CI/CD, API design, MLOps) Building machine learning models and pipelines in Python, using common libraries and frameworks (e.g., TensorFlow, MLFlow) Distributed computing frameworks (e.g., Spark, Dask) Cloud platforms (e.g., AWS, Azure, GCP) and HP computing Containerization and orchestration (Docker, Kubernetes)    Strong problem-solving skills and the ability to analyse issues, identify causes, and recommend solutions quickly Excellent collaboration and communication skills - with teams and customers alike A background in Physics, Engineering, or equivalent Our delivery teams drive innovation to turn AI models into practical solutions -  read our blog to learn more about how you’ll contribute to this exciting journey! What we offer Build what actually matters Help shape an AI-native engin