Staff Software Engineer

About the Role

This is not a conventional engineering role. Our client is an AI-first company rewriting the rules of their segment - building products that put them in direct competition with the likes of Anthropic, Alphabet, and OpenAI. They are not catching up; they are setting the pace.

The engineering culture here is genuinely different. Over 90% of code is written by agentic AI systems. The Staff Engineer they need isn't someone who needs to warm up to that reality - they need someone who hits the ground running inside it, shaping how those systems work, where they fall short, and what comes next.

This is a role for someone who thinks in systems, takes real ownership, and is energised by working at the frontier of what AI-native engineering looks like in practice.

What You'll Do

• Own technical strategy within your domain - understanding the overall architecture and driving decisions that hold up at scale
• Work inside an AI-native engineering environment where agentic systems write the majority of code; your role is to direct, review, extend, and improve what they produce
• Design and build for high-throughput, distributed systems where reliability and performance are non-negotiable
• Bridge engineering and product - bringing a product-minded perspective to technical decisions and translating complexity into outcomes that matter to the business
• Coordinate across engineering, product, and leadership stakeholders; this is not a heads-down IC role - influence and communication are core to how you'll operate
• Take full operational ownership: no separate IT or ops function, your team monitors, responds, and fixes - including out-of-hours on-call rotation
• Run and rotate the scrum champion role within an agile team operating on two-week sprints

What We're Looking For

Must-haves

• Deep, hands-on experience with distributed systems and high-throughput architectures - you understand what breaks at scale and why
• Strong proficiency in Python; comfortable with TypeScript at a working level
• Mastery of design patterns, architectural principles, and systems thinking - this is a role that requires both breadth and depth
• Proven stakeholder management across engineering and product; you see technical problems as product problems and communicate accordingly
• A genuine ownership mindset in a fast-paced environment - you move quickly, take responsibility, and don't wait for permission
• Operational readiness: comfortable being first responder, diagnosing root causes, mitigating and fixing issues, including out-of-hours when it counts
• Experience working in agile environments; confident rotating the scrum champion role and keeping a team accountable to process without being rigid about it

Nice-to-haves

• Hands-on experience with LLM, ML, or AI products - you understand how these systems behave, not just what they claim to do
• Prior exposure to AI-native engineering environments where automation and agentic systems are central to how the team works
• A track record in high-growth or frontier technology companies where the technical bar is set by the best in the world

Your Mindset

• AI-native, not AI-curious. You don't need convincing that agentic systems are the future - you're already operating in that reality and know how to get the best out of them.
• Ownership without prompting. You don't wait for someone to define the problem. You find it, frame it, and move on it.
• Product-minded engineer. You understand that the code you write exists to solve a user or business problem, not to be elegant for its own sake.
• Calm under operational pressure. When things break at 2am, you're the person your team wants on the other end of the call.
• Collaborative, not siloed. You operate across boundaries - engineering, product, leadership - and bring people with you, not just information at them.

Who You Might Have Been

• A Senior or Staff Engineer at a developer tooling, AI infrastructure, or data platform company - someone who has lived the distributed systems problems, not just read about them
• An engineer who moved from pure IC work into technical strategy and stakeholder influence, without losing the ability to go deep
• An early engineer or tech lead at a fast-growth scale-up, where ownership was a necessity and process was something you built, not inherited
• Someone who has worked in or alongside AI/ML engineering teams and understands how models, pipelines, and products fit together