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PhysicsX

Principal Research Scientist

PhysicsX is hiring a Principal Research Scientist in London, United Kingdom. Level rates it ; you can apply on PhysicsX.

Level's read on this job

  • Level rates this research scientist role Builds AI ●●●●: the job is building AI systems.
  • PhysicsX mentions visa sponsorship in this posting.
  • Tools and skills the posting cites include PyTorch, JAX and computer vision.
  • Level classifies it as a lead role.
  • Level lists 40 other open roles at PhysicsX.

AI in this role

PyTorchJAX
computer-vision

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.

Note: We are currently recruiting for multiple levels and positions, however please only apply for the role that best aligns with your skillset and career goals.

What you will do

  • Own Research work-streams at a high-level to deliver outcomes.
    • Align priorities with problem stakeholders, internal and external.
    • Set the technical direction for the stream and apply judgement and taste to drive progress.
    • Plan roadmaps with clear milestones for key decisions and outcomes.
    • Organise and guide the more junior members of the team to effectively execute and deliver against this roadmap.
    • Communicate purpose and key outcomes to raise awareness across the company and create opportunities for use and deployment.
  • Contribute towards Research group strategy and culture.
    • Identify research areas that would be valuable to the company and champion their development, ordering wrt other research objectives.
    • Promote effective working patterns and proactively flag issues with team dynamics to foster a productive environment.
    • Nurture younger colleagues to grow their skillset and guide their professional development.
  • The below activities in particular.
    • Work closely with our machine learning engineers, simulation engineers, and customers 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 and deep learning techniques.
    • Discuss the results and implications of your work with colleagues and customers, especially how these results can address real-world problems.
    • Collaborate with colleagues beyond the research team to translate your models into production-ready code.
    • Communicate your work to others internally and externally as called for in paper publication venues, industry workshops, customer conversations, etc. This will involve writing for academic and non-academic audiences.

What you bring to the table

  • Ability to scope and effectively deliver projects.
  • Enthusiasm about using machine learning, especially deep learning and/or probabilistic methods, 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.
  • PhD in computer science, machine learning, applied statistics, mathematics, physics, engineering, or a related field, with particular expertise in any of the following:
    1. operator learning (neural operators), or other probabilistic methods for PDEs;
    2. geometric deep learning or other 3D computer vision methods for point-cloud or mesh-structured data;
    3. generative models for geometry and spatiotemporal data (VAEs, Diffusion Models, Bayesian non-parametric, scaling to large datasets, etc.).
  • Ideally, >4 years of experience in a data-driven role in a professional industry setting, where you have been instrumental in:
    • building machine learning models and pipelines in Python, using common libraries and frameworks (PyTorch / CUDA, ideally with exposure to JAX, NumPy / SciPy), 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 communication 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 or TPAMI/JMLR.

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.

And it doesn’t stop there …

🚀 Equity options - share meaningfully in the company you’re helping to build.

🏦 10% employer pension contribution - because investing in future matters.

🍽️ Free office lunches - to keep you energised and focused.

👶 Enhanced parental leave - 3 months full pay paternity and 6 months full pay maternity leave, to provide extra flexibility during the moments that matter most.

🍼 YellowNest nursery scheme - to help working parents manage childcare costs.

☀️ 25 days of Annual Leave (+ Public Holidays) - because taking time to rest matters.

🏥 Private medical insurance - 100% employee cover, giving you complete peace of mind.

💪 Wellhub Subscription - gain access to thousands of gyms, classes and wellness apps, supporting both physical and mental wellbeing.

👀 Eye tests - because good work depends on good health.

📈 Personal development - dedicated support for learning, development, and leveling up over time.

💛 Employee Assistance Programme (EAP) - confidential wellbeing support, available whenever you need it.

🚲 Bike2Work scheme and 🚆 Season ticket loan - to make getting to work easier and greener.

🚗 Octopus EV salary sacrifice - for a simpler, more sustainable way to drive electric.

🔎 Watch this space, we’re continuing to build this as we grow…

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 employment and equality legislation. This information is confidential, used only in aggregate form, and will not influence the outcome of your application.

How we rate this

Principal Research Scientist at PhysicsX rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Computer visionPyTorchJAX

Also common in research scientist jobs at this level

Pay

Typical research scientist job
$191k

This posting doesn't list pay. Use the typical research scientist figure when pay comes up. See research scientist jobs

Questions you could be asked

  1. Walk me through a computer vision problem you solved, from raw data to a deployed model.
  2. Walk me through how you've used PyTorch in your day-to-day work.
  3. What are the limits of JAX that you've run into, and how did you work around them?
  4. How would you decide a model or AI system is ready to ship?
  5. Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

  • List these exact terms on your resume: Computer vision, PyTorch, and JAX. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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