Pay
In line with similar jobs
$150K–$190K against $166K–$240K, the middle half of what similar US postings state.
From 1,279 postings in the last year. Stated ranges, not salaries paid.
San Francisco, CA
Open 5 months, first seen by us 154 days agoSalary 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.Read the full posting
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 RoleRead the full posting
Design, build and test reliable, scalable ML data pipelines
Explore and manipulate 3D point cloud and mesh data
Own delivery of technical workstreams
Turn R&D results into reusable libraries, tooling and products
Work with customers on-site to build solutions
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
translate the results of our R&D and projects into re-usable libraries, tooling and products
collaborate closely with customers to build solutions on-site
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.
From the employer’s posting.
PhysicsX is the physics AI company for industrials.
In the employer’s words · Read in context
Open 5 monthsWe first saw this posting 154 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
In line with similar jobs
$150K–$190K against $166K–$240K, the middle half of what similar US postings state.
From 1,279 postings in the last year. Stated ranges, not salaries paid.
Day 154
Up longer than most. Similar jobs usually come down between day 6 and day 69.
At PhysicsX, jobs usually come down by about day 143.
From 4,429 similar postings that came down, and 6 of PhysicsX’s. Coming down is not always a hire.
8 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- 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)
- Distributed computing frameworks (e.g., Spark, Dask) - Cloud platforms (e.g., AWS, Azure, GCP) and HP computing - Containerization and orchestration (Docker, Kubernetes)
- 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
- 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
Complete your application on job-boards.eu.greenhouse.io. The employer’s form will show what is required.
Already applied? Track this application
Yes. We’ve followed 2 other Machine Learning Engineer jobs there since April 2026. One came down after 2 days. One is still up.
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Note: We are currently recruiting for multiple positions, however please only apply for the role that best aligns with your skillset and career goals.
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.
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.
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.
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.
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…
$150,000 - $190,000 depending on experience
Seniority will be assessed throughout our interview process
Selected passages from the saved posting. Check the full description for conditions and exceptions.
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.
San Francisco, CA
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
More relevant text appears in the full description.
No clear work-authorization passage found. Eligibility is unconfirmed.