Senior Machine Learning Engineer, PhysicsX

Salary not provided

AWS

Docker

Kubernetes

GCP

Python

Tensorflow

Azure

Spark

Mid and Senior level

London

2-5 days a week in office

Machine learning for physics & engineering simulations

Job no longer available

Machine learning for physics & engineering simulations

101-200 employees

B2BArtificial IntelligenceDeep TechMachine LearningScience

Job no longer available

Salary not provided

AWS

Docker

Kubernetes

GCP

Python

Tensorflow

Azure

Spark

Mid and Senior level

London

2-5 days a week in office

101-200 employees

B2BArtificial IntelligenceDeep TechMachine LearningScience

Company mission

To reimagine simulation for science and engineering using AI.

Job

Company

Role

Who you are

Enthusiasm about using machine learning for science and engineering
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
(For more experienced candidates) Passion and track record of managing, mentoring and coaching more junior colleagues
Degree in computer science, software engineering or equivalent
3-6 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)
Ability to scope and effectively deliver projects

What the job involves

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, potentially while mentoring more junior colleagues
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

Salary benchmarks

Our take

The rise of generative AI has empowered many industries to fine-tune their operations. PhysicsX utilizes the burgeoning technology to improve the efficiency and advancement of simulations in physics, with applications across aerospace, automotive, renewable energy, medical devices, additive manufacturing, electric vehicles, motorsport, and renewables.

Co-founded by a chief scientist for championship-winning Formula One teams, PhysicsX was born out of a hunger for more advanced research and development phases using numerical physics. R&D and physics simulation go hand-in-hand for many industries, and PhysicsX can help manufacturers iron out problems before they even build a prototype.

Founded in 2020, and raising an impressive $32 million in 2023, the company is pursuing accelerated growth through its customer delivery, product, and research wings. It is about to take the next leap in building out its technology platform and product offering. As it further develops its product, the company has a real chance to capture the market moving forward.

Freddie

Company Specialist at Welcome to the Jungle

Company

Funding (last 2 of 3 rounds)

Nov 2025

$20m

LATE VC

Jun 2025

$135m

SERIES B

Total funding: $187m

Company benefits

Equity package
10% Employer pension contribution
33 days holiday per year with UK bank holidays
Flexible working (remote-friendly, hybrid)
Flexible parental leave (3 months)
Social events (Friday drinks, board game nights, vintage car meet-ups, tech community & art events, etc.)
50 hours per year for learning, training, conferences, volunteering
Bike2Work Scheme

Company HQ

Westminster, London, UK

Leadership

Jacomo Corbo

(CEO)

Previously a Partner at McKinsey & Company, Chief Scientist at QuantumBlack, Senior Research Fellow at Wharton Business School, and Assistant Professor at the University of Ottawa.

Robin Tuluie

(Chairman)

Previously the Head of R&D at Renault (Alpine) F1, and Chief Scientist and Head of R&D for the Mercedes F1 team.

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