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PhysicsX

Physics AI platform for industrial engineering

Machine Learning Engineer - Field Engineering

Applied Machine LearningUpdated on Nov 14
Full-timeNew York, NY, USA

About the Job

Requirements

  • At least 2 years of industry experience in a commercial, non-research environment; the description specifies post-Master's or PhD experience.
  • Experience applying machine-learning methods, including 3D graph and point-cloud deep-learning methods, to real-world engineering applications with measurable impact.
  • Experience in machine-learning, computational-statistics, or modelling use cases in industrial settings, such as supply-chain optimisation or manufacturing processes.
  • At least 2 years of experience in a data-driven role with exposure to software-engineering concepts and best practices, including versioning, testing, continuous integration and continuous delivery, application programming interface design, and MLOps.
  • Experience building machine-learning models and pipelines in Python using libraries and frameworks such as TensorFlow and MLflow.
  • Experience with distributed-computing frameworks such as Spark or Dask.
  • Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform, and high-performance computing.
  • Experience with containerisation and orchestration tools such as Docker and Kubernetes.
  • A background in Physics, Engineering, or equivalent.
  • Ability to analyse issues, identify causes, and recommend solutions quickly.
  • Ability to collaborate and communicate with teams and customers.

Responsibilities

  • Work closely with simulation engineers, data scientists, and customers to understand the physics and engineering challenges being addressed.
  • Design, build, and test reliable, scalable, and easily deployable machine-learning data pipelines.
  • Explore and manipulate 3D point-cloud and mesh data.
  • Own the delivery of technical workstreams.
  • Create cloud or on-premises analytics environments and resources spanning data engineering and data science.
  • Select suitable libraries, frameworks, and tools for tasks and make product-design decisions.
  • Translate research and development and project results into reusable libraries, tooling, and products at the intersection of data science and software engineering.
  • Apply and improve engineering best practices and standards, and coach colleagues in adopting them.
  • Iterate with customers and influence decisions about reliable deployment with measurable outcomes.
  • Travel to customer sites in North America, Europe, Asia, and Oceania for an average of 3–4 weeks per quarter and collaborate with customers on-site.

Desired Qualifications

  • Experience in machine-learning, computational-statistics, or modelling use cases in industrial settings, such as supply-chain optimisation or manufacturing processes, is encouraged.

About the Company

PhysicsX

Physics AI platform for industrial engineering

PhysicsX uses physics-based artificial intelligence for industrial engineering, combining machine learning with physics simulations to shorten design and operation cycles for aerospace, energy, and automotive sectors. Its platform replaces multi-day simulations with near-real-time models that predict performance, helping teams test ideas faster while reducing emissions and costs. It differs from competitors by delivering physics-driven AI with fast, scalable simulations tailored to large, complex industrial systems, supported by a slate of major investors and industrial partners. The company aims to speed up product development and drive broad adoption of physics AI across industrial sectors.

Company size
201-500
Company stage
Series C
Total funding
$487M
Headquarters
London, United Kingdom
Founded
2019

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