Ex-DeepMind Founded Stealth
Founding Research Engineer/Scientist

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About us
We are an early-stage AI company building adaptive systems that understand users, their goals, preferences, and changing context over time.
Our work sits at the intersection of frontier AI, user modelling, cognitive science, and human-computer interaction. The company was founded by a former Google DeepMind Research Scientist (Gemini), and we work closely with researchers at the Consciousness and Cognition Lab at the University of Cambridge on computational models of cognition, subjective experience, and human state.
Our emerging founding team brings together experience from DeepMind, Meta AI, Oxford, and high-scale technology companies, spanning machine learning, personalized systems, and cognitive neuroscience.
Role
We are looking for a full-time, London-based Founding Research Engineer to help build the company’s core technology from the ground up.
You will work directly with the founder across research, engineering, and product, developing systems for:
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.
- user modelling and personalization
- memory and long-term context
- adaptive model behaviour
- evaluation and experimentation
- frontier and open-weight model integration
This is a hands-on founding role. You will translate research ideas into working systems, help define the technical architecture and roadmap, and contribute to the engineering culture and future team.
The role is best suited to someone who combines strong ML engineering with scientific curiosity and wants meaningful ownership at the earliest stage of a company.
Who you are
- 6+ years of professional experience building and deploying production machine learning systems.
- Strong software engineering skills and experience building ML systems end to end.
- Practical experience with LLMs, foundation models, or generative AI.
- Experience building production ML systems, APIs, data pipelines, or distributed software.
- Ability to operate independently, move quickly, and make good decisions under ambiguity.
- Strong communication skills and a collaborative, low-ego working style.
- A desire to take ownership beyond a narrowly defined engineering role.


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Ideally, you have experience...
- Post-training, reinforcement learning, evaluation, or alignment.
- Personalization, recommender systems, user modelling, or memory.
- AI agents, adaptive systems, or human–AI interaction.
- Scalable model infrastructure, distributed training, or inference systems.
- Experience at a frontier AI lab or leading AI startup
- Strong open-source contributions, publications, or other evidence of exceptional technical work.
- PhD in machine learning, computer science, or equivalent industrial experience.
- Strong publication track record (ACL, NeurIPS, ICLR, ICML, etc).
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