Principal Machine Learning Engineer

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!

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…