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Machine Learning Engineer (various levels), AI - London

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Machine Learning Engineer (various levels), AI - London
About the Opportunity
Our client is a well-capitalised, early-stage technology company developing an advanced AI-driven product for consumers. The engineering challenge is significant: the system must perform complex, multi-step reasoning, maintain context over extended interactions, and operate reliably in production despite the inherent unpredictability of large models.
The organisation is deliberately lean - a small group of senior, high-calibre engineers who move quickly, make decisions collectively, and hold a high bar for both quality and pace. The mission is to deliver a product experience that feels genuinely different from what's currently on the market.
The Roles
Our client is looking to hire multiple profiles into their ML Technical staff. As a Member of Technical Staff, Machine Learning, you will build core ML components and work directly on production systems from day one - gaining first-hand exposure to how large-scale ML behaves outside a research setting. This role suits engineers who want to build strong systems judgement through shipping, debugging, and iterating on real-world ML, alongside more senior colleagues.
Focus Areas
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
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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.
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- Build and improve ML components spanning data, training, evaluation, and inference
- Fine-tune and adapt models as part of larger production systems
- Implement evaluation and testing frameworks to understand model behaviour
- Contribute to data pipelines covering both real-world and synthetic data
- Debug model issues, performance problems, and production incidents
- Ship improvements iteratively, guided by real user feedback
- Work closely with senior ML engineers and product teams
- Operate comfortably within the constraints of a live production system - latency, cost, reliability, and safety all matter simultaneously
What Good Looks Like in This Role
- Production ML models meet expected accuracy, latency, and reliability targets
- Production issues are identified quickly, debugged effectively, and resolved at the root cause
- Data pipelines, training loops, and inference systems are robust, reproducible, and maintainable
- Works effectively across engineering, product, and research to deliver reliable ML-powered features
- Improvements to models and systems are driven by real-world signals and measurable outcomes
Technical Environment


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- Python
- PyTorch / JAX
- Production ML systems running on GPU infrastructure
Candidate Profile
- Strong foundations in machine learning and modern neural network architectures
- Some hands-on experience training, fine-tuning, or deploying ML models
- Comfortable writing production-quality code and picking up new tools quickly
- Curious, coachable, and keen to learn from real systems in production
- Able to work through ambiguity with guidance, growing ownership over time
- A natural bias toward shipping, iteration, and continuous improvement
To apply or discuss this role further please send your CV to Don Fletcher via don.fletcher@ap-executive.com
AP Executive are working with a well capitalised early stage AI firm looking to expand their ML Team.
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