Principal Machine Learning Engineer

PhysicsX • New York, NY

2d • $140,000 - $190,000 • Hybrid

About The Position

PhysicsX is hiring a Principal Machine Learning Engineer in Delivery to lead the development and deployment of machine learning solutions for manufacturing and maintenance, repair and overhaul (MRO). The role focuses on applying ML to real-world industrial process and sensor data, which may be sparse, drifting, miscalibrated, or incomplete. The successful candidate will work directly with customers, often on-site, and collaborate with Data Scientists, Simulation Engineers, Manufacturing Process Experts, and customers to define engineering problems and deploy reliable solutions with measurable shop-floor outcomes.

The role owns the ML approach from modelling through deployment in customer production environments, including cloud, on-premises, and edge settings. It involves selecting between physics simulations, reduced-order models, and learned models; combining simulation results with manufacturing measurements; establishing uncertainty quantification and validation standards; and making models transferable across machines, materials, and consumable batches. The engineer will shape product requirements for manufacturing and MRO, lead data and ML system architecture, build end-to-end training and inference pipelines and APIs, and mentor engineers and data scientists. The position also involves communicating technical trade-offs to senior stakeholders and process owners, contributing to project scoping and customer development, and travelling worldwide to customer sites an average of 3–4 weeks per quarter.

PhysicsX describes itself as a physics AI company for industrials, building simulation software to enable physics AI across the engineering lifecycle. It partners with organisations in aerospace and defence, automotive, semiconductors, materials, and energy and renewables. The company is headquartered in the UK, with offices in London, New York, and Singapore and an expanding presence in the Bay Area. This position uses a hybrid model combining time in the New York office with work-from-home days.

Requirements

• At least 5 years of industry experience after a Master's degree or Ph.D. in a commercial, non-research environment.
• Experience putting machine learning into production on industrial process or sensor data rather than only benchmark datasets.
• Experience building and deploying ML using manufacturing process data such as parameter logs, sensor traces, inspection records, or small DoE datasets.
• Experience shipping ML systems end to end and at scale, including designing, building, and testing reliable, scalable ML data pipelines.
• Ability to work with sparse, drifting, miscalibrated, and partly unrecorded equipment data and judge whether it is sufficient for training.
• Ability to translate R&D and project outputs into reusable libraries, tooling, and products.
• Ability to lead technical initiatives, make pragmatic product and architecture decisions, and mentor others.
• Ability to work directly with customers and communicate with senior stakeholders and process owners.
• Experience building training and inference pipelines and APIs using tools such as Python, PyTorch, Pandas, FastAPI, SciPy, and Kubeflow.
• Willingness to travel to customer sites worldwide, averaging 3–4 weeks per quarter.

Nice To Haves

• Experience in aerospace or another regulated manufacturing environment.

Responsibilities

• Own the machine learning approach for manufacturing and MRO data, from modelling through deployment in customer production environments across cloud, on-premises, and edge settings.
• Build production ML solutions using sparse, drifting, miscalibrated process and sensor data, and assess whether available data is sufficient for training.
• Choose between physics simulation, reduced-order models, and learned models, and combine simulation output with shop-floor measurements where they disagree.
• Set uncertainty quantification and validation standards for process recommendations against qualified process windows.
• Design experiments where machine time is a constrained cost.
• Develop models that transfer across machines, material lots, and consumable batches.
• Define manufacturing and MRO product requirements for the PhysicsX platform.
• Lead scoping and architecture for data and ML systems.
• Communicate results and technical trade-offs to senior stakeholders and process owners.
• Build end-to-end training and inference pipelines and APIs, including for 3D point-cloud and mesh data.
• Mentor engineers and data scientists.
• Travel to customer sites worldwide, averaging 3–4 weeks per quarter.
• Participate in scoping new projects and bringing new customers to PhysicsX.

Benefits

• Equity options.
• 5% contribution to 401(k).
• Free team lunch once per week.
• Private health insurance.
• Enhanced parental leave: 3 months of full-pay paternity leave and 6 months of full-pay maternity leave.
• 20 days of annual leave plus public holidays.
• Personal development support for learning and development.
• Subsidized Gympass / Wellhub for the employee and up to 3 family members.
• Flexible Spending Account (FSA) for eligible healthcare expenses.

What This Job Offers

• Job Type: Full-time
• Career Level: Principal
• Education Level: No Education Listed