Machine Learning Engineer

PhysicsX is hiring a Machine Learning Engineer in San Francisco, United States. It pays $150k-$190k a year and Level rates it ; you can apply on PhysicsX.

Level's read on this job

Level rates this machine learning engineer role Builds AI ●●●●: the job is building AI systems.
The posting mentions visa sponsorship.
The posting mentions TensorFlow, MLflow and Python among its tools and skills.
Level classes it as a mid-level role.
PhysicsX has 38 other open roles on Level.

AI in this role

Design, build, and deploy physics-aware AI models and scalable ML pipelines for industrial engineering applications.

tensorflowmlflowpython

ml-opsmachine-learningdata-pipelinessoftware-engineeringgeometry-modelling

About us

Re-architecting Engineering for the Age of Intelligence

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.

Note: We are currently recruiting for multiple positions, however please only apply for the role that best aligns with your skillset and career goals.

Who We're Looking For

As a Machine Learning Engineer in Delivery, you are a problem solver who stays anchored to impact. You are someone who can grasp advanced engineering concepts across multiple industries, and excel at working directly with customers (and often side-by-side with them on-site) to embed cutting-edge AI models into tools that are useful and used.

You’ve shipped ML systems end-to-end and at scale: you design, build and test reliable, scalable ML data pipelines; you know how to explore and manipulate 3D point-cloud and mesh data to enable geometry-aware modelling; you select the right libraries, frameworks and tools. 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 2 years industry experience (post Masters or PhD) in a commercial, non-research environment. You're truly excited about taking ownership of complex work streams and guiding teams to success, while continuously improving the systems and solutions you work on to ensure they are practical, impactful and meet the evolving needs of our customers.

We have hybrid offices in London, New York, and Singapore; this role is hybrid based in the San Francisco area.

This Role

As a Machine Learning Engineer, you'll work closely with our Data Scientists, Simulation Engineers, and customers to understand and define the engineering and physics challenges we are solving. You will iterate with customers and use your influence to drive decisions around reliable deployment with measurable outcomes.

What you will do

Work closely with our simulation engineers, data scientists and customers to develop an understanding of the physics and engineering challenges we are solving
Design, build and test data pipelines for machine learning that are reliable, scalable and easily deployable
Explore and manipulate 3D point cloud & mesh data
Own the delivery of technical workstreams
Create analytics environments and resources in the cloud or on premise, spanning data engineering and science
Identify the best libraries, frameworks and tools for a given task, make product design decisions to set us up for success
Work at the intersection of data science and software engineering to translate the results of our R&D and projects into re-usable libraries, tooling and products
Continuously apply and improve engineering best practices and standards and coach your colleagues in their adoption

You'll also have the opportunity to travel to customer sites in North America, Europe, Asia, Oceania, for an average of 3-4 weeks per quarter, where you'll collaborate closely with customers to build solutions on-site. What you bring to the table

Experience applying Machine learning methods (including 3D graph/point cloud deep learning methods) to real-world engineering applications, with a focus on driving measurable impact in industry settings.
Experience in ML/Computational statistics/Modelling use-cases in industrial settings (for example supply chain optimisation or manufacturing processes) is encouraged.
A track record of scoping and delivering projects in a customer facing role
2+ years’ experience in a data-driven role, with exposure to software engineering concepts and best practices (e.g., versioning, testing, CI/CD, API design, MLOps)
Building machine learning models and pipelines in Python, using common libraries and frameworks (e.g., TensorFlow, MLFlow)
Distributed computing frameworks (e.g., Spark, Dask)
Cloud platforms (e.g., AWS, Azure, GCP) and HP computing
Containerization and orchestration (Docker, Kubernetes)
Strong problem-solving skills and the ability to analyse issues, identify causes, and recommend solutions quickly
Excellent collaboration and communication skills - with teams and customers alike
A background in Physics, Engineering, or equivalent

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.

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.

🏥 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…

Salary range:

$150,000 - $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.

How we rate this

Machine Learning Engineer at PhysicsX rates 85 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Machine Learning Engineer is an open job at PhysicsX.
Level rates this role AI Level 4 of 4: Builds AI.
The listed salary is $150k-$190k a year.
The role is based in San Francisco, CA.
The job was posted on June 2, 2026.
Seniority: mid.
Applications are handled by PhysicsX. Apply on PhysicsX

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

ML OpsMachine learningData PipelinesSoftware EngineeringGeometry ModellingTensorFlowMLflowPython

Also common in machine learning engineer jobs at this level

PyTorch LLM Generative AI C++Fine-tuning Reinforcement learning

Pay

This job starts at: $150k
Typical machine learning engineer job: $200k

This posting starts 25% below the typical machine learning engineer job. See machine learning engineer jobs

Questions you could be asked

How do you monitor a model once it's live, and how do you know it needs retraining?
Tell me about a project where machine learning was part of your work. What did you do?
Tell me about a project where data pipelines was part of your work. What did you do?
Tell me about a project where software engineering was part of your work. What did you do?
Tell me about a project where geometry modelling was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: ML Ops, Machine learning, Data Pipelines, Software Engineering, and Geometry Modelling. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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