PhysicsX
Machine Learning Engineer
$150K–$190K
New York, United States +1 · Full-time · Posted Apr 23, 2026
- Location
- New York, United States
New York, New York, United States - Experience
- At least 2 years of industry experience post-Masters or PhD in a commercial, non-research environment; also states 2+ years’ experience in a data-driven role.
- Education
- Background in Physics, Engineering, or equivalent.
- Visa sponsorship
- An offer may be conditioned on authorization to receive software or technology controlled under U.S. export control laws and regulations without sponsorship for an export license.
About the job
Build and deliver reliable machine-learning systems and reusable tools that apply AI to industrial engineering challenges, working closely with scientists, engineers, and customers.
What you'll do
- Design, build, and test scalable, deployable ML data pipelines; explore and manipulate 3D point-cloud and mesh data.
- Own technical workstreams and develop cloud or on-premise analytics environments and resources.
- Work with customers and technical teams to translate R&D into reusable libraries, tooling, and products, and support deployment with measurable outcomes.
What they ask for
- Experience applying machine-learning methods, including 3D graph or point-cloud deep learning, to real-world engineering applications.
- 2+ years’ experience in a data-driven role, with exposure to software engineering concepts and best practices.
- Build ML models and pipelines in Python; experience with common libraries and frameworks.
- Background in Physics, Engineering, or equivalent.
Nice to have
- Industrial ML, computational statistics, or modelling experience is encouraged.
- Customer-facing project scoping and delivery experience; experience with distributed computing, cloud platforms, containers, or orchestration.
Pay
$150,000 - $190,000 USD base salary
Tools
Python, TensorFlow, MLFlow, Spark, Dask, AWS, Azure, GCP, Docker
AI focus
Apply machine learning, including 3D graph and point-cloud deep learning, to industrial engineering and physics problems; deliver practical AI solutions and reusable tools.
Model-written summary · the original posting is authoritative