Machine Learning Engineer
Confirmed live in the last 24 hours
PhysicsX

51-200 employees

AI-driven physics simulations for advanced industry optimization
Company Overview
PhysicsX stands out as a leading deep-tech company, leveraging machine learning to significantly expedite physics simulations, thereby opening up new avenues for optimization in physical design and engineering. Their work has far-reaching implications across a variety of critical industries such as Space, Aerospace, Medical Devices, and Renewables, with tangible societal benefits like enhancing artificial heart designs, reducing CO2 emissions, and boosting renewable turbine performance. The company's commitment to continuous improvement, trust-based relationships, and real-world implementation of their breakthrough technologies, coupled with their profitable and sustainable growth, make it an attractive workplace for engineers and scientists.
AI & Machine Learning
Energy
Aerospace

Company Stage

Series A

Total Funding

$32M

Founded

N/A

Headquarters

London, United Kingdom

Growth & Insights
Headcount

6 month growth

64%

1 year growth

200%

2 year growth

325%
Locations
London, UK
Experience Level
Entry
Junior
Mid
Senior
Expert
Desired Skills
Kubernetes
Microsoft Azure
Python
Apache Spark
Docker
AWS
Data Analysis
Google Cloud Platform
CategoriesNew
AI & Machine Learning
Requirements
  • Degree in computer science, software engineering or equivalent
  • 2+ years’ experience in a data-driven role
  • Experience with software engineering concepts and best practices
  • Experience in building machine learning models and pipelines in Python
  • Knowledge of distributed computing frameworks
  • Familiarity with cloud platforms and HP computing
  • Ability to scope and effectively deliver projects
  • Strong problem-solving skills
  • Excellent collaboration and communication skills
Responsibilities
  • Work closely with simulation engineers, data scientists, and customers to understand physics and engineering challenges
  • Design, build, and test data pipelines for machine learning
  • 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
  • Identify the best libraries, frameworks, and tools for a given task
  • Translate the results of R&D and projects into re-usable libraries, tooling, and products
  • Apply and improve engineering best practices and standards
Desired Qualifications
  • Enthusiasm about using machine learning for science and engineering
  • Experience with distributed computing frameworks like Spark and Dask
  • Familiarity with cloud platforms such as AWS, Azure, GCP
  • Knowledge of containerization and orchestration (Docker, Kubernetes)