Principal ML Engineer — Industrial AI, Equity & Deployment

PhysicsX Ltd.; New York (NY); Hybrid; USD 180,000 - 250,000; Full time

Benefits offered by this job

Equity options

401(k)

Free team lunch 1x/week

Private health insurance

Enhanced parental leave

20 days Annual Leave

Personal development

Gympass / Wellhub

Flexible Spending Account (FSA)

Job summary

PhysicsX Ltd. in New York is seeking a Principal Machine Learning Engineer in Delivery to turn cutting‑edge AI into practical manufacturing solutions.

You will lead end‑to‑end ML deployments on industrial data, mentor engineers, and work directly with customers on‑site when needed to embed models into production workflows. You’ll design scalable ML data pipelines, balance physics simulation with learned models, and set validation standards.

Qualifications

• With at least 5 years of industry experience (post Masters or PhD) in a commercial, non‑research environment.
• Experience shipping ML systems end to end in production environments.
• Ability to lead scoping and architecture for data and ML systems.

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 and judge data sufficiency for training.
• Define product requirements for the PhysicsX platform and lead tech initiatives.
• Lead scoping and architecture for data/ML systems and communicate results to stakeholders.
• Build end‑to‑end training and inference pipelines and APIs (Python, PyTorch, Pandas, FastAPI, SciPy, Kubeflow).
• Travel to customer sites worldwide (3–4 weeks per quarter).

Skills

Production ML

Data pipelines

Python

PyTorch

Kubeflow

Leadership

Tools

PyTorch

Pandas

FastAPI

SciPy

Kubeflow

3D data processing

Job description

PhysicsX Ltd. in New York is seeking a Principal Machine Learning Engineer in Delivery to turn cutting‑edge AI into practical manufacturing solutions.

You will lead end‑to‑end ML deployments on industrial data, mentor engineers, and work directly with customers on‑site when needed to embed models into production workflows. You’ll design scalable ML data pipelines, balance physics simulation with learned models, and set validation standards.