Machine Learning Scientist
New York
Delivery /
Full-time /
Hybrid
PhysicsX is a deep-tech company with roots in numerical physics and Formula One, dedicated to accelerating hardware innovation at the speed of software.
We are building an AI-driven simulation software stack for engineering and manufacturing across advanced industries. By enabling high-fidelity, multi-physics simulation through AI inference across the entire engineering lifecycle, PhysicsX unlocks new levels of optimization and automation in design, manufacturing, and operations — empowering engineers to push the boundaries of possibility. Our customers include leading innovators in Aerospace & Defense, Materials, Energy, Semiconductors, and Automotive.
We are about to take the next leap in building out our technology platform and product offering. In this context, we are looking for a capable and enthusiastic machine learning engineer to join our team. If all of this sounds exciting to you, we would love to talk.
Note: We do not provide visa sponsorship in the US. Please only apply if you have the right to work in the US.
What you will do
- Work closely with our simulation engineers, machine learning engineers and customers to develop an understanding of the physics and engineering challenges we are solving
- Build innovative models to predict the behaviour of physical systems leveraging state-of-the-art machine learning and deep learning techniques
- Own the delivery of data science workstreams
- Design, build and test data pipelines for machine learning that are reliable, scalable and easily deployable
- Discuss the results and implications of your work with your colleagues and our customers
- Contribute to our internal R&D and product work
What you bring to the table
- Enthusiasm about using machine learning, especially deep-learning and/or probabilistic methods, for science and engineering
- Ability to scope and effectively deliver projects
- 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
- Degree in computer science, machine learning, applied statistics, mathematics, physics, engineering or a related field
- Helped build machine learning models and pipelines in Python, using common libraries and frameworks (e.g., NumPy, SciPy, Pandas, TensorFlow, PyTorch, Airflow), especially including deep-learning applications
- Software engineering concepts and best practices for collaborative programming (e.g., versioning, testing, deployment)
- Working in a cloud environment with one of the major cloud providers
- (Appreciated, but not a prerequisite) Simulations for FEA/CFD
What we offer
- Be part of something larger: Make an impact and meaningfully shape an early-stage company. Work on some of the most exciting and important topics there are. Do something you can be proud of
- Work with a fun group of colleagues that support you, challenge you and help you grow. We come from many different backgrounds, but what we have in common is the desire to operate at the very top of our fields and solve truly challenging problems in science and engineering. If you are similarly capable, caring and driven, you'll find yourself at home here
- Experience a truly flat hierarchy. Voicing your ideas is not only welcome but encouraged, especially when they challenge the status quo
- Work sustainably, striking the right balance between work and personal life.
- Receive a competitive compensation and equity package, in addition to plenty of perks
$120,000 - $240,000 a year
Final salary will be based on experience.
Our stance
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.
