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 and Defense, Materials, Energy, Semiconductors, and Automotive.We are about to take the next leap in building out our technology platform and product offering. In this context, we are looking for a capable and enthusiastic machine learning engineer to join our team. If all of this sounds exciting to you, we would love to talk.Note:

We are currently recruiting for multiple positions, however please only apply for the role that best aligns with your skillset and career goals.What you will doWork closely with our simulation engineers, data scientists and customers to develop an understanding of the physics and engineering challenges we are solvingDesign, build and test data pipelines for machine learning that are reliable, scalable and easily deployableExplore and manipulate 3D point cloud and mesh dataOwn the delivery of technical workstreamsCreate analytics environments and resources in the cloud or on premise, spanning data engineering and scienceIdentify the best libraries, frameworks and tools for a given task, make product design decisions to set us up for successWork at the intersection of data science and software engineering to translate the results of our RandD and projects into re-usable libraries, tooling and productsContinuously apply and improve engineering best practices and standards and coach your colleagues in their adoption

What you bring to the tableExperience 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/ComputationalStatistics/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 role2+ 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 computingContainerization and orchestration (Docker, Kubernetes)Strong problem-solving skills and the ability to analyse issues, identify causes, and recommend solutions quicklyExcellent collaboration and communication skills - with teams and customers alikeA background in Physics, Engineering, or equivalent

What we offerEquity options share in our success and growth.10% employer pension contribution invest in your future.Free office lunches great food to fuel your workdays.Flexible working balance your work and life in a way that works for you.Hybrid setup enjoy our new Shoreditch office while keeping remote flexibility.Enhanced parental leave support for lifes biggest milestones.Private healthcare comprehensive coveragePersonal development access learning and training to help you grow.Work from anywhere extend your remote setup to enjoy the sun or reconnect with loved ones.

We value diversity and are committed to equal employment opportunity regardless of sex, race, religion, ethnicity, nationality, disability, age, sexual orientation or gender identity. We strongly encourage individuals from groups traditionally underrepresented in tech to apply. To help make a change, we sponsor bright women from disadvantaged backgrounds through their university degrees in science and mathematics.#J-18808-Ljbffr