AI RSL Tooling Specialist

Team Wheel; Greater London; Vor Ort; GBP 80.000 - 110.000; Vollzeit

Benefits dieser Stelle

Shadow equity

Flexible working

Zusammenfassung

Team Wheel Limited is seeking an AI Tooling Specialist to build self-improving AI systems in London. You will design and implement recursive learning loops, automated evaluation, and memory mechanisms, taking ownership from architecture to production.

Ideal candidates have strong Python experience with LLMs, experience designing iterative improvement loops, and familiarity with evaluation design and prompt/chain optimisation.

Qualifikationen

• Strong Python and hands-on experience building with LLMs at a production level.
• Demonstrable experience designing recursive or iterative self-improvement loops in AI systems.
• Deep familiarity with evaluation design — automated scoring, preference modelling, critic models.
• Experience with prompt/chain optimisation frameworks (DSPy, TextGrad, or equivalent).
• Solid understanding of agent architectures and multi-step reasoning pipelines.
• Ability to articulate why a self-learning loop failed, not just that it did.

Aufgaben

• Design recursive learning loops where AI outputs are scored and fed back into generations.
• Build self-critique and self-refinement pipelines for improved responses before delivery.
• Implement Constitutional AI-style approaches to drive automated improvement without human-in-the-loop on every iteration.
• Design memory and state mechanisms to apply learnings across sessions and tasks.
• Develop automated evaluation frameworks (evals) and feedback signal pipelines.

Kenntnisse

Python programming

LLMs in production

Recursive self-improvement

Evaluation design

Prompt/chain optimisation

Agent architectures

Troubleshooting self-learning loops

Tools

DSPy

TextGrad

Jobbeschreibung

The Role

This role exists for one reason: to build AI systems that get better on their own.

We're looking for an AI Tooling Specialist with a deep focus on recursive self-learning — the design and implementation of systems that evaluate their own outputs, identify failure modes, and update their own behaviour without requiring constant human intervention. You'll own this capability end-to-end at Team Wheel, from architecture through to production.

If you've spent time thinking seriously about how to make AI systems that genuinely improve over time — not just ones that are prompted better — this is the role for you.

We believe in paying fairly and being upfront about it. For this role, we're offering a base salary of £80,000–£110,000 depending on experience, with additional performance-related pay. We know the people we're looking for have options, and we want them invested in what we're building together so we are open to building a creative package for the right individual, possibly including shadow equity.

What You'll Do

Design and Build Self-Improving Systems

• Architect recursive learning loops in which AI outputs are automatically scored, critiqued and fed back into subsequent generations

• Build self-critique and self-refinement pipelines where models evaluate and iteratively improve their own responses before delivery

• Implement Constitutional AI-style or critic-model approaches to drive automated quality improvement without human-in-the-loop on every iteration

• Design memory and state mechanisms that allow systems to accumulate and apply learnings across sessions and tasks

Build the Evaluation Layer

• Develop robust, automated evaluation frameworks (evals) that measure output quality across multiple dimensions

• Create feedback signal pipelines — combining automated scoring, preference signals and sparse human feedback — to drive continuous improvement

• Build regression detection so the system flags when it is getting worse, not just when it is getting better

• Instrument all systems for observability: you'll know at any point whether the loop is working

Automate the Optimisation Process

• Implement automated prompt and chain optimisation (DSPy, TextGrad, or similar) so that the system tunes itself rather than relying on manual prompt engineering

• Build processes that generate, test and promote improved instructions or configurations autonomously

• Work towards systems that can propose and validate their own architectural changes within defined constraints

Integrate and Deploy

• Build integrations with the platforms Team Wheel works with (Workday and other HR SaaS)

• Own reliability and performance in production — these are live systems, not experiments

• Document the architecture, the learning mechanisms, and the failure modes clearly

What We're Looking For

Essential

• Strong Python and hands-on experience building with LLMs at a production level

• Demonstrable experience designing recursive or iterative self-improvement loops in AI systems

• Deep familiarity with evaluation design — automated scoring, preference modelling, critic models

• Experience with prompt/chain optimisation frameworks (DSPy, TextGrad, or equivalent)

• Solid understanding of agent architectures and multi-step reasoning pipelines

• You can articulate clearly why a self-learning loop failed — not just that it did

Desirable

• Background in or strong understanding of RLHF, DPO, or related preference optimisation techniques

• Experience with Constitutional AI, self-critique, or debate-style architectures

• Exposure to enterprise HR platforms or complex API integrations

• Published work, open source contributions, or personal projects in self-improving AI systems

Why Join Team Wheel

• Own a genuinely novel capability inside a growing consultancy

• Production systems from day one — not research, not prototypes

• Work alongside domain experts who understand the HR tech context deeply

• A culture that stays close to the frontier and invests in doing this properly

• Competitive salary and flexible working

Team Wheel is an equal opportunities employer. We welcome applications from candidates of all backgrounds.