# Applied Scientist - All Levels at PhysicsX

PhysicsX is hiring an Applied Scientist - All Levels in Singapore, Singapore. Level rates it Builds AI ●●●●; you can [apply on PhysicsX](https://jobsbylevel.com/go/d1278c61-06a4-4814-b430-e697f6f007a9?utm_medium=ai-agent).

AI Level 4, AI centrality 90 out of 100. Singapore .

## Details

- Company: [PhysicsX](https://jobsbylevel.com/companies/physicsx)
- AI level: AI Level 4 (score 90 out of 100)
- Location: Singapore 
- Posted: July 8, 2026
- Apply: https://jobsbylevel.com/go/d1278c61-06a4-4814-b430-e697f6f007a9?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=applied-scientist-all-levels-at-physicsx-0d60c7

## Prepare for this job

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## Description

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. PhysicsX is starting a research team in Singapore to build physical foundation models alongside our customers and partners, targeting engineering domains where this capability will be most transformative. What you will do Work closely with our machine learning engineers, simulation engineers, customers and partners to translate physics and engineering challenges into mathematical problem formulations. Build models to predict the behaviour of physical systems using state-of-the-art machine learning techniques that scale to large datasets, iterating through robust experimentation. Chart a path through competing trade-offs with insufficient information, e.g. is it better to train a bigger model or to generate more data? Own Research work-streams at different levels, depending on seniority. Discuss the results and implications of your work with colleagues and customers, connecting with real-world problems. Communicate your work to others internally and externally as called for in paper publication venues, industry workshops, customer conversations, etc. Foster curiosity and initiative among your colleagues and mentees. 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. PhD in computer science, machine learning, applied statistics, mathematics, physics, engineering, or a related field, with particular expertise in any of the following: operator learning (neural operators), or other probabilistic methods for PDEs; geometric deep learning or other 3D computer vision methods for point-cloud or mesh-structured data; generative models for geometry and spatiotemporal data (VAEs, Diffusion Models, Bayesian non-parametric, scaling to large datasets, etc.). Ideally, >2 years of experience in a data-driven role, with exposure to: building machine learning models and pipelines in Python, using common libraries and frameworks (e.g., NumPy, SciPy, Pandas, PyTorch, JAX), especially including deep learning applications; developing models for bespoke problem settings that involve high-dimensional data (spatiotemporal, geometric, physical); iterating on network architectures and model structure, tuning and optimising for inductive biases, improved generalisability, and improved performance; combining theoretical reasoning with empirical intuition to guide investigation; formulating and running experiment pipelines to benchmark models and produce comparable results; writing skills for communicating complex technical concepts to peers and non-peers, tailoring the message for the required audience. Publication record in reputable venues that demonstrates mastery in your field, and in particular the domains of interest listed above. Desirable venues include (but not limited to): NeurIPS, ICML, ICLR, UAI, AISTATS, AAAI, Siggraph, CVPR, TPAMI/JMLR, Nature and Science. What we offer Build what actually matters Help shape an AI-native engineering company at a formative stage, tackling

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