Lead Software Engineer, Bite Engineering

• A love of learning a new domain, and shipping something quickly that actually fixes what was broken
• A track record of shipping and owning production systems end to end. Years matter less to us than evidence you've done it
• Experience hiring, growing and leading a small engineering team
• Strong backend depth, ideally Python in production, and comfortable working across a React/TypeScript frontend
• Experience owning architecture for data-heavy systems: ingestion pipelines, queues, workers and storage
• Experience putting LLM or ML features into production, and measuring whether they're actually right
• Genuine product sense so you can independently weigh technical trade-offs against customer value
• The instinct to keep things simple under pressure, and the judgement to know when a tactical shortcut is right and when it isn't
• Clear communication across deeply technical, semi-technical and plain-language conversations. Contractors buy outputs they can audit, so explaining the working matters
• Don't rule yourself out over the stack. We're looking for a strong engineer with sound principles. Technologies are tools

Desirable

• We're a well-funded startup using AI to decrease risk and grow margin in the architecture, engineering and construction industry
• Our current focus is general contractors. When they bid to build a major project, such as a skyscraper or an airport, they price millions of pounds' worth of project risks, and treatments for them, by hand, and often inaccurately
• During delivery, risk treatments that were suboptimal or never applied erode the margin
• Despite billions in annual revenue, contractors only keep ~2%
• Bite puts contractors' data to work at tender and through delivery, so the bid price rests on evidence and the margin predicted at tender is the margin realised
• Bite ingests the whole tender pack, auto-builds the risk register for the team to review, and prices it with Monte Carlo simulation reconciled against a reference class of similar completed projects. Through delivery, an AI auditor tracks risks and treatments, and every completed project improves the next tender
• You'll lead engineering at Bite, setting the technical direction, staying hands-on in the code, and building the team that ships it. You'll work closely with Phil Cowans, our fractional CTO, who co-founded Songkick and Nested as CTO and studied Bayesian machine learning at Cambridge (the maths behind probabilistic risk pricing)
• The platform is in production on a Python backend and a React/TypeScript frontend on Azure. You don't need experience in construction or in our stack. We hire for engineering judgement, and the team treat specific technologies as tools for tackling problems
• The problem is genuinely hard. A tender pack is thousands of pages of drawings, specifications, contracts and bills of quantities, with a tight deadline for bids. Turning it into a structured risk register, then into a defensible bid price checked against similar completed internal and external projects, is the core of our product. Every number has to show its working, because contractors won't trust things they can't interrogate and deeply understand
• You'll own engineering outright, not a slice of it. There's no engineering organisation to inherit and no layer of management to negotiate with. What exists today is a working platform and a small team; the architecture, standards, hiring bar and the shape of the function as it grows are yours to decide
• You'll build it in the room with the customer. We're co-developing the product on real data with some of the world's largest general contractors. You'll hear what works and what doesn't directly from bid and risk teams
• Own the technical direction of the platform, from tender-pack ingestion to the risk engine, and write and review code on the critical path yourself
• Turn messy tender packs and contractors' project archives into structured, citable data the risk model can trust
• Take the risk model from research to production with Phil: reference class forecasting, Monte Carlo pricing, and the evals proving accuracy
• Hire and grow the engineering team, and set the bar every subsequent hire is measured against
• Establish how engineering operates as it scales: standards, code review, release, on-call, and how work gets prioritised