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Zenithon
World models for extreme physics
- —
- BACLVSS+3
- London
- AI
Open roles at Zenithon
4 positions we're tracking, re-checked daily
- apply ↗
ML Research Engineer (scientific ML / neural operators)
londonMid-levelfirst seen today - apply ↗
Computational Physicist (plasma, CFD or MHD)
londonMid-levelfirst seen today - apply ↗
ML Infrastructure / HPC Engineer
londonMid-levelfirst seen today - apply ↗
Forward Deployed Engineer (US customers)
londonMid-levelfirst seen today
Know when Zenithon is hiring before anyone else
A role stays uncontested for about four days. Here's the window, and where we put you in it.
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- live
- +2h+2hrole spotted & verified1
- +4h+4hyou get the alert1
- day 3day 3you've applied~8
- day 7+day 7+hits the job boards250+
What Zenithon is building
Zenithon builds what it calls world models for extreme physics: learned models of how plasmas, combustion and process chambers behave, trained to predict the outcome of a design without running the full simulation. The targets are named and specific: fusion reactors, rockets and semiconductor manufacturing. The company's claim is that its models can explore a million design points in the time it takes a conventional solver to simulate one. Two details separate this from generic surrogate modelling. First, the models train on both simulation output and real experimental data, so they can be corrected by what actually happened in the lab rather than only by what the solver said should happen. Second, predictions ship with uncertainty estimates, which is the only way an engineer at a fusion company will trust a learned model enough to cut a simulation run. The company plans to release new models every quarter. The team composition tells you what this is. The CTPO wrote a radiation hydrodynamics code from scratch during his Imperial PhD and built MHD models at General Fusion. NYU's Zongyi Li, one of the researchers behind neural operator methods for PDEs, is an adviser, alongside Commonwealth Fusion Systems co-founder Dan Brunner. This is a physics lab using ML, not an ML lab that discovered physics.
Why this matters
Design cycles in fusion, propulsion and fabs are bottlenecked by simulation cost: a single high-fidelity plasma or combustion run can take days on a cluster, so engineers explore a handful of designs instead of thousands. As reported by Tech Funding News, the global simulation software market was valued at $26.5B in 2025 and private fusion companies had raised $4.48B through July 2026. That is a lot of capital chasing hardware whose iteration speed is set by solvers written decades ago. If a learned model can tell you which thousand designs are worth simulating properly, the expensive solver becomes the verification step, not the search step.
Investors: Backed, Lunar Ventures, Seraphim Space, MMC Ventures, SOSV, Angels: founders and hyperscaler directors
Hiring outlook
Explicit: per Tech Funding News, Zenithon is going from 11 to 17 full-time staff within three to six months, and half the $10M goes to hiring. Tech.eu reports the team will expand in San Francisco and across the US.
Working at Zenithon
Zenithon is a AI company based in London. For a AI company this size, the reality is opportunity to shape your role based on the company stage.
Most Zenithon jobs are based in London.
How to actually get hired at Zenithon
Why applying the normal way doesn't work
Zenithon has an ATS but the real pipeline is referrals → sourced → recruiter picks → cold apps. You can still get through, but know where you stand.
Who to contact at Zenithon
- Who decides
- the hiring manager
- Best channel
- LinkedIn or direct email
What to show them
A one-page breakdown of a real design loop at a US fusion, launch or fab company you know: what solver they use, how long a run takes, and which decision a 1,000x faster approximate answer would actually change.
A cold email that works at Zenithon
What Zenithon screens for
The cap table is a deliberate mix: Seraphim is a space specialist (rockets), MMC and Backed are deep-tech generalists, SOSV backs hard-tech, and Lunar invests in technical founders building new compute paradigms. Each covers one of the three target industries. The advisers do the same work: Brunner for fusion credibility, Li for ML method credibility, Songhurst for strategy. The hiring window is narrow and specific: six seats in the next few months, half the budget on people, and a fresh US footprint with no one in it yet. A US-based candidate with real simulation experience is exactly what the SF expansion needs.
Don't send a generic CV to Zenithon. Mirror the job posting's language to get past automated screening.
Don't make these mistakes
Pitching yourself as an ML generalist who finds physics interesting. Abetharan wrote a radiation hydrodynamics code from scratch and built MHD models at General Fusion; he will know within a sentence whether you understand why a surrogate breaks near a shock or an instability. Alex left a fusion PhD because progress was too slow, so 'AI will revolutionise science' is exactly the vague optimism he left. Lead with a specific solver, a specific failure mode, and what you did about it.
Mistakes that kill Zenithon applications
At people, a copy-paste CV is immediately obvious. It's an instant no.
Lead with their problem, not your ambition. Zenithon is focused on Design cycles in fusion, propulsion and fabs are bottlenecked by simulation cost: a single high-fidelity plasma or combustion run can take days on a cluster, so engineers explore a handful of designs instead of thousands — show you understand that.
Don't apply and wait. The median AI application gets no response ever. One follow-up at day five roughly doubles reply rates.
Applying to Zenithon? Get the contact, not the form.
The Zenithon interview process
We don't yet have verified candidate reports for Zenithon. What follows is the typical process for a -person AI company, so treat it as a model, not confirmed detail.
Interview stages
- 1
Recruiter Screen
Phone or video · 30 min
What it testsBasic qualification and logisticsUsually run byRecruiter or HR - 2
Hiring Manager Interview
Video call · 45 min
What it testsRole fit and experience deep-diveUsually run byHiring manager - 3
Technical/Functional Round
Video call · 60 min
What it testsSkills assessment and problem-solvingUsually run byTeam members - 4
Final Round
In-person or video · 60 min
What it testsCulture fit and cross-functional alignmentUsually run bySenior leadership
Zenithon take-home assignment
Zenithon includes a take-home exercise in their interview process. For AI roles, this typically involves a practical problem that takes 2-4 hours. Focus on clean, working code over premature optimization. They're evaluating how you think and communicate, not just the solution.
Zenithon interview timeline
At 14 days, Zenithon's process is about average than typical for AI (14 days at this size).
Interviewed at Zenithon?
Tell us how it went: stages, questions, timeline. Takes 90 seconds and it's how this page stays accurate for the next person.
Zenithon jobs, frequently asked questions
How many jobs does Zenithon have open?
Zenithon currently has 4 open roles, last verified October 2, 2026.
Does Zenithon hire remotely?
Zenithon doesn't have remote openings at the moment. All roles are in London.
What roles is Zenithon hiring for?
Zenithon is hiring across Engineering, Other. The most recent opening is ML Research Engineer (scientific ML / neural operators).
How do I apply for a job at Zenithon?
Apply directly through the links above, or read our guide on how to actually get hired at Zenithon.
Does Zenithon respond to cold emails?
We're still collecting cold email data for Zenithon.
Who is the hiring manager at Zenithon?
At this size, hiring is usually run by the hiring manager.
How competitive is it to get hired at Zenithon?
Expect 100-250 applicants in the first two weeks for AI roles at this size. Apply within 72 hours for best odds.
How many rounds is the Zenithon interview?
4 stages: Recruiter Screen, Hiring Manager Interview, Technical/Functional Round, Final Round.
Is the Zenithon interview hard?
It concentrates on technical depth and system design rather than abstract puzzles. The stage candidates find hardest is Technical Interview.
Does Zenithon give a take-home task?
Yes, Zenithon includes a take-home assignment.
How long does Zenithon take to get back to you?
Around 14 days across the full process.
What should I prepare for the Zenithon interview?
Study technical depth and system design. At this size (), they care about self-sufficiency over textbook knowledge.
Where is Zenithon based?
Zenithon is headquartered in London, US.
Get Zenithon roles before they're posted
A role stays uncontested for about four days. Here's the window, and where we put you in it.
From $5.99/week, cancel any time.
- live
- +2h+2hrole spotted & verified1
- +4h+4hyou get the alert1
- day 3day 3you've applied~8
- day 7+day 7+hits the job boards250+
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