--- title: 'Principal Machine Learning Engineer at PhysicsX' canonical: 'https://feeny.ai/job/principal-machine-learning-engineer-physicsx-new-york-3d9h60fwps9r' type: 'job' last_seen: '2026-11-05' --- # Principal Machine Learning Engineer at PhysicsX - **Company:** PhysicsX - **Location:** New York, NY - **Posted:** 2026-11-03 - **Last confirmed live:** 2026-11-05 - **Apply:** https://job-boards.eu.greenhouse.io/physicsx/jobs/5026120101 ## Job description ## About us Re-architecting Engineering for the Age of Intelligence PhysicsX is the physics AI company for industrials. The company’s mission is to accelerate hardware innovation by overhauling what industrial engineering and manufacturing look like today. PhysicsX is building a new simulation software stack to deliver deep physics AI enablement across the entire engineering lifecycle. The company partners with leading organisations in aerospace & defence, automotive, semiconductors, materials, and energy & renewables, supporting them on some of their most critical and complex challenges. PhysicsX is headquartered in the United Kingdom, with offices in London, New York, and Singapore and an expanding presence in the Bay Area. Who We're Looking For As a Principal Machine Learning Engineer in Delivery, you are an experienced problem solver and technical leader who stays anchored to impact. You can grasp advanced engineering concepts across multiple industries, lead technical initiatives, and work directly with customers (often side by side on-site) to embed cutting-edge AI models into tools that are useful and used. For this role, that means manufacturing and maintenance, repair and overhaul (MRO). You have put ML into production on industrial process or sensor data, not benchmarks. You know the difference between a model that scores well offline and one a process owner will accept on a qualified production line. Your data comes off equipment rather than out of a solver: it is sparse, drifting, miscalibrated and partly unrecorded, and you know how to work with it. You've shipped ML systems end to end and at scale: you design, build and test reliable, scalable ML data pipelines, select the right libraries, frameworks and tools, and make pragmatic product decisions that set Delivery up for success. 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 5 years of industry experience (post Masters or PhD) in a commercial, non-research environment, you're ready not only to execute but to lead and mentor others. This Role You'll work closely with Data Scientists, Simulation Engineers, Manufacturing Process Experts and customers to define the process and physics problems we are solving, and you'll drive decisions on reliable deployment with measurable outcomes on the shop floor. You'll: - Own the ML approach to manufacturing and MRO data, from modelling through deployment in customer production environments (cloud, on-prem and edge). - Build production ML on sparse, drifting, miscalibrated process and sensor data (process logs, sensor traces, inspection records, small DoE sets), and judge whether the data is enough to train on. - Choose between physics simulation, reduced-order and learned models, and fuse simulation with shop-floor measurement where the two disagree. - Set the UQ and validation standard for what a process recommendation may claim against a qualified process window, and design experiments where machine time is the cost. - Make models transfer across machines, material lots and consumable batches. - Define the product requirements that manufacturing and MRO place on the PhysicsX platform. - Lead scoping and architecture for data and ML systems, and communicate results and trade-offs to senior stakeholders and process owners. - Build end-to-end training and inference pipelines and APIs (Python, PyTorch, Pandas, FastAPI, SciPy, Kubeflow, among others), including for 3D point-cloud and mesh data, and mentor engineers and data scientists. - Travel to customer sites worldwide, on average 3-4 weeks per quarter, and take part in scoping new projects and bringing new customers to PhysicsX. ## What you've done We hire on what you've done, so strong candidates can point to things like: - Built and deployed ML on manufacturing process data (parameter logs, sensor traces, inspection records or small DoE sets) and can say what changed in production as a result - Fused simulation output with shop-floor measurement where the two disagreed - Set UQ and validation standards that other teams followed - Shaped a product or tooling roadmap based on what a site or company actually needed - Had a recommendation rejected by a process owner, and understood why Experience in aerospace or another regulated manufacturing environment is a plus. It is not required, because the must-have bar can be met in any sector where data comes off equipment. 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!](https://www.physicsx.ai/newsroom/how-delivery-makes-physical-ai-work-in-the-real-world)https://www.physicsx.ai/newsroom/how-delivery-makes-physical-ai-work-in-the-real-world ## What we offer Build what actually matters Help shape an AI-native engineering company at a formative stage, tackling problems that genuinely matter for industry and society. This is work with real-world impact - and something you can be proud to stand behind. Learn alongside exceptional people Work with a high-caliber, collaborative team of engineers, scientists, and operators who care deeply about doing great work, and about helping each other get better. We come from diverse backgrounds, but we share a commitment to operating at the highest level and addressing some of the most complex challenges out there. If you’re ambitious, thoughtful, and driven by impact, you’ll feel at home. Influence over hierarchy We operate with a flat structure: good ideas win - wherever they come from. Questioning assumptions and challenging the status quo isn’t just welcomed, it’s expected. Sustainable pace, long-term ambition Building meaningful technology is a marathon, not a sprint. We believe in balancing focused, ambitious work with a life beyond it. Our hybrid model blends time together in our New York office with work-from-home days, giving you the flexibility to work sustainably while staying connected in person. And it doesn’t stop there … 🚀 Equity options - share meaningfully in the company you’re helping to build. 💰 5% contribution to 401(k) - build long-term security with a strong retirement plan. 🍽️ Free team lunch 1x/week - good food, great company, and space to connect. 🏥 Private health insurance – comprehensive cover for you, offering total peace of mind. 👶 Enhanced parental leave – 3 months full pay paternity and 6 months full pay maternity leave, to provide extra flexibility during the moments that matter most. ☀️ 20 days of Annual Leave (+ Public Holidays) - because taking time to rest matters. 📈 Personal development – dedicated support for learning, development, and leveling up over time. 💪 Gympass / Wellhub (subsidized) – for you and up to 3 family members, supporting both physical and mental wellbeing. 💳 Flexible Spending Account (FSA) – set aside pre-tax dollars for eligible healthcare expenses. 🔎 Watch this space, we’re continuing to build this as we grow… 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. We collect diversity and inclusion data solely for the purpose of monitoring the effectiveness of our equal opportunities policies and ensuring compliance with employment and equality legislation. This information is confidential, used only in aggregate form, and will not influence the outcome of your application. ## About PhysicsX ## Company Overview - **One-liner**: PhysicsX builds an AI-native software stack for physics simulation and engineering design, enabling advanced industries to accelerate innovation by orders of magnitude. - **Entity Type**: Private (Series C) - **Headquarters**: London, United Kingdom - **Founded**: 2019 - **Founders**: Jacomo Corbo (CEO & Co-Founder) and a Co-Founder & Chairman ## Core Business - **Primary industries**: Aerospace & Defense, Automotive, Semiconductors, Materials, Energy & Renewables - **Target customers**: Large industrial organizations (B2B Enterprise) – engineers and teams designing and operating complex physical systems - **Mission**: Empower industrial organizations to build beyond human imagination and address the defining challenges of our time, with a focus on climate and human health impact ## Products & Services - **PhysicsX Platform** (SaaS + embedded engineering): An AI-native software stack that unifies simulation, physics AI, data, and engineering applications. Integrates with existing toolchains (CAD, CAE, PLM) and accelerates simulation by orders of magnitude, enabling engineers to explore larger design spaces and move from concept to validated design in days. ## Market Standing - **Valuation**: Approximately $2.4B (as of Series C in August 2026) - **Key Metric**: Total funding ~$487M (including $300M Series C, $135M Series B, $32M Series A, $20M from NVentures). Revenue is generating but not publicly disclosed (LinkedIn estimates ~$1M, but company is early-stage revenue generating). - **Notable Investors/Partners**: Atomico (led Series B), General Catalyst (led Series A), NVentures (NVIDIA’s venture arm), NVIDIA (strategic partner), Siemens (collaboration on data center power infrastructure) - **Growth Signals**: - Series C oversubscribed $300M at $2.4B valuation (Aug 2026) - Headcount growth of 71% YoY (LinkedIn: 226 employees; PitchBook: 350 employees post-C) - Forward-deployed engineers embedded in customer programs - Open standards for physics AI powered by NVIDIA (March 2026) ## Competitive Advantages - **True AI-native platform**: Physics AI is built as a core primitive, not bolted onto legacy tooling. Uses advanced models like Fourier Neural Operators to simulate physics at speeds unattainable with traditional CFD/FEA. - **Embedded deployment model**: Forward-deployed engineers work alongside customers on live problems, ensuring measurable outcomes and deep domain integration. - **Focus on hardest problems**: Targets extreme complexity (aerospace, semiconductors, climate tech) where speed of development is critical and margins for error are zero. - **Strategic partnerships**: Deep collaboration with NVIDIA (compute, open standards) and Siemens (infrastructure AI). ## Strategic Focus - Continue scaling the platform across multiple industrial verticals, with emphasis on the climate transition (energy, materials efficiency) and human health (medical devices, supply chain). - Expand the "Large Physics Models" research program and open standards initiatives. - Grow the global team (currently 350+ across UK and US) with strong emphasis on research, product engineering, and forward deployment. ## Why Work Here - **Culture**: Grounded visionaries, moving fast with trust and ambition. Values include "we make stuff happen" and "we form a community in which people can grow." - **Work environment**: Hybrid (2-3 days in office per week); offices in London (Shoreditch) and New York (Downtown Manhattan). Visa sponsorship and relocation support available. - **Benefits**: Generous pension contributions, health insurance, enhanced parental support, development opportunities. - **Engineering culture**: Work on the hardest problems in industry – physics, AI, and engineering collide. Small, high-autonomy teams. Interview process includes pairing and in-person final rounds. - **Diversity**: Committed to equal opportunities; strongly encourages underrepresented groups to apply. ## Sources 1. [PhysicsX.ai](https://www.physicsx.ai/) – company website 2. [PhysicsX About](https://www.physicsx.ai/about) – mission, team, challenge 3. [PhysicsX Careers](https://www.physicsx.ai/careers) – culture, benefits, interview process 4. [LinkedIn](https://uk.linkedin.com/company/physicsx) – employee count, funding, headcount growth 5. [PitchBook](https://pitchbook.com/profiles/company/529644-70) – valuation, Series C, founding year ## Other roles at PhysicsX - [Head of Chemistry](https://feeny.ai/job/head-of-chemistry-physicsx-new-york-nn2v0mz1ebdd) — New York, NY - [Head of Chemistry](https://feeny.ai/job/head-of-chemistry-physicsx-london-4fm0gnh9danw) — London, United Kingdom - [Platform Engineer - Self Hosting LLMs](https://feeny.ai/job/platform-engineer-self-hosting-llms-physicsx-london-e21a8r33hznz) — London, United Kingdom - [Deployment Strategist](https://feeny.ai/job/deployment-strategist-physicsx-new-york-p6db3byb2a0m) — New York, NY - [Senior People Operations Manager](https://feeny.ai/job/senior-people-operations-manager-physicsx-new-york-fzc1c1tjtbkn) — New York, NY - [Platform Support Engineer](https://feeny.ai/job/platform-support-engineer-physicsx-singapore-k3rwa8fcz73f) — Singapore - [Simulation Engineer (CFD) - Large Physics Models](https://feeny.ai/job/simulation-engineer-cfd-large-physics-models-physicsx-london-4e2nb7eenrxj) — London, United Kingdom - [Simulation Engineer - Thermal Process](https://feeny.ai/job/simulation-engineer-thermal-process-physicsx-singapore-69rhqzweam43) — Singapore - [Platform Support Engineer](https://feeny.ai/job/platform-support-engineer-physicsx-new-york-an8gzcdfwjbw) — New York, NY - [Delivery Engineer](https://feeny.ai/job/delivery-engineer-physicsx-singapore-5wsdrs5mzky7) — Singapore