---
title: 'Applied Scientist - All Levels at PhysicsX'
canonical: 'https://feeny.ai/job/applied-scientist-all-levels-physicsx-singapore-0204xnrranns'
type: 'job'
last_seen: '2026-09-10'
---

# Applied Scientist - All Levels at PhysicsX

- **Company:** PhysicsX
- **Location:** Singapore
- **Posted:** 2026-07-08
- **Last confirmed live:** 2026-09-10
- **Apply:** https://job-boards.eu.greenhouse.io/physicsx/jobs/4922319101

## Job description

## About us

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.

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 problems that genuinely matter for industry and society. This is work with real-world impact - and something you can be proud to stand behind.

Learn alongside exceptional people

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.

Influence over hierarchy

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.

Sustainable pace, long-term ambition

Building meaningful technology is a marathon, not a sprint. We believe in balancing focused, ambitious work with a life beyond it. Our hybrid model blends time together in our Shoreditch office with work-from-home days, giving you the flexibility to work sustainably while staying connected in person.

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.

We collect diversity and inclusion data solely for the purpose of monitoring the effectiveness of our equal opportunities policies and ensuring compliance with UK employment and equality legislation. This information is confidential, used only in aggregate form, and will not influence the outcome of your application.

## About PhysicsX

## Company Overview
- **One-liner**: PhysicsX builds an AI-native software stack for physics simulation and engineering design, enabling advanced industries to accelerate innovation by orders of magnitude.
- **Entity Type**: Private (Series C)
- **Headquarters**: London, United Kingdom
- **Founded**: 2019
- **Founders**: Jacomo Corbo (CEO & Co-Founder) and a Co-Founder & Chairman

## Core Business
- **Primary industries**: Aerospace & Defense, Automotive, Semiconductors, Materials, Energy & Renewables
- **Target customers**: Large industrial organizations (B2B Enterprise) – engineers and teams designing and operating complex physical systems
- **Mission**: Empower industrial organizations to build beyond human imagination and address the defining challenges of our time, with a focus on climate and human health impact

## Products & Services
- **PhysicsX Platform** (SaaS + embedded engineering): An AI-native software stack that unifies simulation, physics AI, data, and engineering applications. Integrates with existing toolchains (CAD, CAE, PLM) and accelerates simulation by orders of magnitude, enabling engineers to explore larger design spaces and move from concept to validated design in days.

## Market Standing
- **Valuation**: Approximately $2.4B (as of Series C in August 2026)
- **Key Metric**: Total funding ~$487M (including $300M Series C, $135M Series B, $32M Series A, $20M from NVentures). Revenue is generating but not publicly disclosed (LinkedIn estimates ~$1M, but company is early-stage revenue generating).
- **Notable Investors/Partners**: Atomico (led Series B), General Catalyst (led Series A), NVentures (NVIDIA’s venture arm), NVIDIA (strategic partner), Siemens (collaboration on data center power infrastructure)
- **Growth Signals**: 
  - Series C oversubscribed $300M at $2.4B valuation (Aug 2026)
  - Headcount growth of 71% YoY (LinkedIn: 226 employees; PitchBook: 350 employees post-C)
  - Forward-deployed engineers embedded in customer programs
  - Open standards for physics AI powered by NVIDIA (March 2026)

## Competitive Advantages
- **True AI-native platform**: Physics AI is built as a core primitive, not bolted onto legacy tooling. Uses advanced models like Fourier Neural Operators to simulate physics at speeds unattainable with traditional CFD/FEA.
- **Embedded deployment model**: Forward-deployed engineers work alongside customers on live problems, ensuring measurable outcomes and deep domain integration.
- **Focus on hardest problems**: Targets extreme complexity (aerospace, semiconductors, climate tech) where speed of development is critical and margins for error are zero.
- **Strategic partnerships**: Deep collaboration with NVIDIA (compute, open standards) and Siemens (infrastructure AI).

## Strategic Focus
- Continue scaling the platform across multiple industrial verticals, with emphasis on the climate transition (energy, materials efficiency) and human health (medical devices, supply chain).
- Expand the "Large Physics Models" research program and open standards initiatives.
- Grow the global team (currently 350+ across UK and US) with strong emphasis on research, product engineering, and forward deployment.

## Why Work Here
- **Culture**: Grounded visionaries, moving fast with trust and ambition. Values include "we make stuff happen" and "we form a community in which people can grow."
- **Work environment**: Hybrid (2-3 days in office per week); offices in London (Shoreditch) and New York (Downtown Manhattan). Visa sponsorship and relocation support available.
- **Benefits**: Generous pension contributions, health insurance, enhanced parental support, development opportunities.
- **Engineering culture**: Work on the hardest problems in industry – physics, AI, and engineering collide. Small, high-autonomy teams. Interview process includes pairing and in-person final rounds.
- **Diversity**: Committed to equal opportunities; strongly encourages underrepresented groups to apply.

## Sources
1. [PhysicsX.ai](https://www.physicsx.ai/) – company website
2. [PhysicsX About](https://www.physicsx.ai/about) – mission, team, challenge
3. [PhysicsX Careers](https://www.physicsx.ai/careers) – culture, benefits, interview process
4. [LinkedIn](https://uk.linkedin.com/company/physicsx) – employee count, funding, headcount growth
5. [PitchBook](https://pitchbook.com/profiles/company/529644-70) – valuation, Series C, founding year

## Other roles at PhysicsX

- [IT Lead Engineer](https://feeny.ai/job/it-lead-engineer-physicsx-london-q1fk60zjd3m8) — London, United Kingdom
- [General Application - Join Our Talent Community](https://feeny.ai/job/general-application-join-our-talent-community-physicsx-singapore-ff82t2dkq2w4) — Singapore
- [CFD Engineer](https://feeny.ai/job/cfd-engineer-physicsx-new-york-ed2z3r3afh4y) — New York, NY
- [Delivery Operations Manager](https://feeny.ai/job/delivery-operations-manager-physicsx-london-6nb5k6xmx8fw) — London, United Kingdom
- [Forward Deployed Software Engineer](https://feeny.ai/job/forward-deployed-software-engineer-physicsx-london-ctmsk9sjcj5a) — London, United Kingdom
- [Staff Backend Software Engineer - GO & Python](https://feeny.ai/job/staff-backend-software-engineer-go-python-physicsx-london-jya69fmtw4c3) — London, United Kingdom
- [Strategy & Programs Manager](https://feeny.ai/job/strategy-programs-manager-physicsx-london-gn01bh125dtg) — London, United Kingdom
- [Deployment Strategist - Aerospace & Defense](https://feeny.ai/job/deployment-strategist-aerospace-defense-physicsx-new-york-navkewnrbqwc) — New York, NY
- [Senior Simulation Engineer - Electromagnetics Specialist](https://feeny.ai/job/senior-simulation-engineer-electromagnetics-specialist-physicsx-new-york-q1a4azp2whvc) — New York, NY
- [CFD Engineer](https://feeny.ai/job/cfd-engineer-physicsx-singapore-4fc35cfjnfyh) — Singapore