Workonomics
Research Engineer
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💫 Company | AI-native developer platform
📈 Stage | Series A, $100m+ raised 📏 Size | ~50 people and growing 🧢 Role | AI Research Engineer (2 hires) 🎯 Areas | harness engineering, agentic orchestration, evals ✨ Stack | Python, TypeScript, applied ML/LLM tooling 📍 Based | King's Cross, London 💻 Hybrid | 3 days in-office 💰 Offer | £140–180k + equity
Background
Most companies are making today's development workflows faster. This one is asking a different question: what does software development actually look like when agents are contributing meaningfully across design, implementation, and iteration?
This is a ~50-person company, backed by world-class investors, building the platform to provide the context, skills, and governance layer that makes AI-native software development actually work at enterprise scale.
What you'd be working on
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A core part of this role is building their software production system designed to run largely autonomously, where agents handle the majority of build, test, and delegation work with minimal human intervention.
Your workstreams will centre on research and experimentation: figuring out what actually works, what breaks, and how to build this into something customers can run themselves. You'll be encouraged to publish public research on your findings, contributing to one of the most active and contested conversations in AI-native engineering right now.
Who fits here
A Research Engineer with:
- a Masters or PhD in a relevant computational field
- experience shipping AI/ML products in a startup or applied industry setting
- strong product and customer instincts
who can also bring depth in at least one of these areas:
- Agent harness & orchestration - designing how an agent's tools, context, and control flow fit together so it can actually get things done reliably in production
- Eval methodology - building the tests and metrics that tell you whether an agent is genuinely working
- Feedback loops & pipeline design - going beyond a single agent run: building systems that turn results into improvements over time, including reinforcement-style optimisation
- Failure analysis at scale - digging into why agents fail, by instrumenting them and reading through large volumes of logs/traces to find patterns the engineering team can actually fix
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The problem they're solving - what software development becomes in an AI-native world - is one of the most consequential open questions in the industry right now.
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