Principal Machine Learning Engineer

Teli Labs; New York (NY); On-site; USD 140,000 - 190,000; Full time

Benefits offered by this job

Equity options

5% 401(k) contribution

Free team lunch 1x/week

Private health insurance

Enhanced parental leave

20 days annual leave (+ holidays)

Personal development support

Gympass / Wellhub

Flexible Spending Account

Job summary

PhysicsX is hiring a Principal Machine Learning Engineer in New York, NY. You will lead the design and deployment of production ML systems for manufacturing and MRO data, working closely with data scientists and customers on-site to embed cutting-edge AI into tools used on the shop floor.

With 5+ years in industry (post Masters/PhD), you will mentor teams, define architecture for ML data systems, and drive measurable outcomes. Travel 3–4 weeks per quarter to customer sites worldwide.

Qualifications

• Experience shipping ML systems in production on industrial data.
• Ability to mentor engineers and data scientists.
• Experience collaborating with on-site customers.
• Strong track record in data and ML system architecture.

Responsibilities

• 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).
• 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 standards for process recommendations against a qualified window; design experiments where machine time is costing.
• Make models transferable 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; communicate results and trade-offs to stakeholders.
• Build end-to-end training and inference pipelines and APIs (Python, PyTorch, Pandas, FastAPI, SciPy, Kubeflow, among others).
• Travel to customer sites worldwide, 3–4 weeks per quarter.

Skills

Production ML

ML deployment

Python

PyTorch

Pandas

FastAPI

SciPy

Kubeflow

3D data

Mesh data

Education

Master's degree or PhD in a related field

Tools

PyTorch

Kubeflow

FastAPI

Job description

About Us

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.

Principal Machine Learning Engineer

New York, United States

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.

What We Offer

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.

Salary Range

• $140,000 - $190,000 depending on experience

Seniority will be assessed throughout our interview process.

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.