We're looking for an influential Staff AI Engineer to join our small AI Engineering team at Lendable to help us enable developers to move faster whilst keeping quality high.
Our mission is to bring all the lessons we have learned about AI development so far into one system so everyone can benefit. The system combines our Lendable Coding Agent, for measuring effectiveness of AI and the stability of our systems.
This is a role where you'll be working with our engineers and product managers who build our products. You will improve the developer experience and improve our throughput. You’ll see instant impact with your work and participate in a highly collaborative environment.
We need someone who takes full ownership - not just writing code, but thinking through the problem, designing the solution, shipping it, and making sure it keeps working. You'll own your work from "what should we build?" through to "is it still delivering value?"
You'll also be working at the frontier of AI tooling - building with LLMs, experimenting with new approaches, and figuring out what's possible.
What you'll be doing
Build on top of our AI cloud coding agent
Build the core of our AI harness
Enable capabilities in these agents for specification, testing, and service reliability
Work with security and data governance requirements to ensure the agents are secure and have access to the appropriate data
Enable others to build with AI
Encourage teams to level up their AI Software development processes
Enable teams to build AI actors that plug into the SDLC
Help teams measure the impact AI is having on the development process
Transplant good ideas and processes from one team to other teams in the organization
What we're looking for
Essential
6+ years of software engineering experience in a mid-to-large-size organisation
Experience of building AI tooling to improve developer experience (devex)
The ability to influence how people work across multiple teams
Proven experience building AI tooling used by others in a commercial environment
Strong full-stack skills in TypeScript
Frontend skills with Next.js or React to build an interface for engineers
Strong AI knowledge — LLMs, embeddings, MCPs, RAG, evals, agentic platforms
Experience of building infrastructure to support your systems in production using IaC, k8s or lambda functions
Self-starter who takes ownership end-to-end — from understanding the problem, through design and implementation, to monitoring and iteration
Motivated by impact — you want to see your work used and know it's making a difference
Nice to have
Experience with Cloudflare infrastructure
Experience with monitoring tools such as Datadog, Sentry, Grafana
Experience with creating guardrails for products: SLOs, DORA metrics
How you'll work
You’ll be part of a small core team that works with engineering representatives from across the company. We have a vision of how we can accelerate the engineering process with AI, and you’ll be working with this group to complete the plan. We will also experiment and adapt the plan as new AI tooling and approaches emerge.
We value shipping and learning over perfection. The goal is always to deliver something useful, learn from how it's used, and improve. You won't be directly client-facing, but your work will directly impact colleagues across the business — and you'll hear about it when something you built makes their day easier.
Why join?
See your work make a difference This isn't a team where your code disappears into a monolith. You'll build something, ship it and get instant feedback from developers. Every integration and tool you ship has a direct line to engineering efficiency.
High leverage A small core team means your contributions have outsized impact. No layers, fast decisions, real ownership.
Build new things We're building a platform from the ground up, not maintaining legacy systems. You'll shape how AI gets used across Lendable.
Work at the frontier AI tooling is moving fast. You'll work with the latest in agentic AI, workflow orchestration and LLM tooling — applied to real problems, not just proof-of-concepts.
Interview process
Screening call with Hiring Manager
Take-home task
Technical interview based on the task
Interview with Engineering Manager
Final interview with Hiring Manager
