Skip to content
NEXTMOVEFDE careers · United States

Senior Machine Learning Engineer

Technologies

Python · PyTorch · Pandas · fastAPI · Scipy · Kubeflow · 3D point-cloud · mesh data · CAE · CFD · FEA

About PhysicsX

AI-native simulation software stack that replaces slow numerical physics with deep-learning surrogates for design, manufacturing, and operations in aerospace, automotive, semiconductors, energy, and materials.

Series B · 100–200 people

Job description

The full responsibilities and requirements are on the employer’s site.

Read the job description
Source and classification

Production engineering · Evidence for this classification:

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 Senior Machine Learning Engineer in Delivery,
More from the job description

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 Senior Machine Learning Engineer in Delivery, you are an experienced problem solver and technical leader who stays anchored to impact. You are someone who can grasp advanced engineering concepts across multiple industries, lead technical initiatives, 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 explo [... source excerpt omitted ...] 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: This position may require access to information protected under U.S. export control laws and regulations, including the Export Administration Regulations (EAR) and the International Traffic in Arms Regulations (ITAR). Please note that any offer for employment may be conditioned on authorization to receive software or technology controlled under these U.S. export control laws and regulations without sponsorship for an export license. This Role As a Senior MLE, you'll work closely with [... source excerpt omitted ...] 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. You'll: Own the deployment of ML models and engineering surrogates (e.g., deep learning on CAE/CFD/FEA data, time‑series forecasting, anomaly detection, optimization & control) to customer production environments. Communicate results and trade‑offs to senior stakeholders; steer roadmaps and influence product direction with evidence. Lead scoping and architecture design for data/ML systems; define success metrics, delivery plans and quality bars. Excel at building robust and scalable ML systems, tr

How jobs are selected

Employer postings · Data from · Sources