SpAItial
ML Inference Engineer

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About SpAItial
SpAItial is pioneering the next generation of World Models, pushing the boundaries of generative AI, computer vision, and the simulation of reality. We are moving beyond 2D pixels to build models that natively understand the physics and geometry of our world. Our mission is to redefine how industries, from robotics and AR/VR to gaming and cinema, generate and interact with physically-grounded 3D environments.
Role: Research Engineer
We’re seeking a Research Engineer to own the backend that serves our world models. You will build and run the inference systems behind our product and API, turning frontier research models into fast, reliable, and scalable services. This is a hands-on role for someone who has already owned production APIs in an AI system and can work closely with researchers to bring new models to users.
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
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Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
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Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
Responsibilities
- Own the inference services and APIs that power our product, from request to generated output.
- Optimize serving performance, reliability, and cost at scale. Maximize GPU utilization and minimize device memory footprint.
- Apply latest techniques for improving inference performance.
- Deploy research models as production services, including customer-specific configurations.
- Work with research and product colleagues to bring new models to users.
Key qualifications
- 3+ years of software engineering in an AI, machine learning, or computer vision product environment.
- Experience designing and owning production APIs.
- Strong Python, and experience shipping containerized services on GPU inference platforms.
- Hands-on GPU inference performance work: profiling, memory, batching, and cold start.
- Familiarity with 3D or computer vision data is a strong plus.


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About Us
At SpAItial, we are committed to creating a diverse and inclusive workplace. We welcome applications from people of all backgrounds, experiences, and perspectives. We are an equal opportunity employer and ensure all candidates are treated fairly throughout the recruitment process.
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