Founding Optimisation Engineer

Dayjob.AI · YC P26

London, Moorgate · Full-time · On-site or in Office (4 days/week) · Data Science & Operations Research

Dayjob builds AI agents for short-haul trucking - a $45BN market that still runs on spreadsheets, phone calls, and software from the 1990s. We’re in the last YC batch (P26), scaling fast toward $1M ARR, growing 20% MoM and launching in the US. Our first customers are some of the largest operators in UK and US waste and recycling.

George (CEO) and Fred (CTO) founded Dayjob after meeting at Oxford - between them, they’ve launched and scaled products at Deliveroo and Otta, with 15 years of AI-driven supply chain optimisation experience. They’re joined by a team of engineers, operators, and customer success leads who’ve built and scaled products at Amazon, Sky, and beyond.

Every day, millions of operational decisions get made across industrial fleets: what work is booked, which vehicles fulfil it, and what happens when things go wrong. Most of it is still managed by hand. We’re replacing it with AI agents that reason in real time - paired with an interface planners actually want to use.

The software running the physical economy has never been well optimised. We’re going to fix that.

Deploying state-of-the-art algorithms into legacy businesses is one way to think about what we do at Dayjob. Hence, optimisation is the cornerstone of what we do; saving 1% time on the road gives our customers hundreds of thousands more revenue and saves gallons of diesel on wasted mileage.

We’ve launched models to optimise roll-off vehicles in the waste sector; this is just the tip of the iceberg.

• Build and own new models that power Dayjob - routing, scheduling and assignment across different verticals of industrial fleets
• Design, test, and scale optimisation models in real-world environments, then productionise them with the engineering team
• Work directly with customers to understand the constraints and edge cases that actually decide a plan’s quality
• Build the tooling to evaluate plan quality, trade-offs, and impact - so we can prove the gain in money, not just in theory
• Improve runtime, robustness, and solution quality as we scale across more vehicles, customers, and geographies
• Shape Dayjob’s long-term optimisation and data strategy - and, as we grow, help hire and define the company culture.

You think in trade-offs, not ideals. Real fleets don’t hand you a clean objective function. You’re comfortable balancing competing constraints in a fast-changing system and knowing which ones actually matter to the customer.

You go where the work is. The best modelling decisions in this product come from understanding how the job really gets done - so you’re willing to gather requirements from operational users and deeply understand what drives them, not just read the spec.

You ship, then sharpen. You prototype quickly, get it live, and improve against real results. You know when to reach for a solver, when a heuristic wins, and when the honest answer is more data.

You’re rigorous and pragmatic. You care about correctness and solution quality, and you can hold that bar while moving at startup speed.

You’re AI-native in how you work. You already use Claude, Cursor, or similar to extend what you can build, and you see AI as a partner in your own practice.

• A degree in mathematics, physics, computer science, engineering, or a similar quantitative field
• 4+ years working on optimisation, routing, scheduling, or applied OR problems - ideally with meaningful time on real, deployed systems
• A track record of building and shipping optimisation or ML solutions, ideally in a fast-paced transport or logistics setting
• Experience in working with geospatial data like spectral analysis, applied graph theory and computational geometry.
• Strong Python and SQL, with hands-on experience using solvers (e.g. OR-Tools, Gurobi, CPLEX)
• The ability to balance theoretical rigour with pragmatic constraints, and to own a model end-to-end: design → build → deploy → tune
• Comfort working with messy operational data and edge-case-heavy workflows
• Experience mentoring or leading other engineers, or a clear and genuine ambition to start

• Vehicle routing problems (VRP), especially with uncertainty and problem sizes requiring decomposition
• Constraint programming or metaheuristics
• Real-time decision systems
• Logistics, fleet, or field-service products
• Early-stage startups

• Competitive salary
• Significant equity
• 25 days holiday + your birthday off
• Moorgate office
• Learning & development budget
• A foundational role defining our optimisation engine and technical roadmap from the ground up

1. Intro call with the team (30 mins)
2. Technical interview (2 hrs)
3. Final chat with the founders (45 mins)

Short-haul trucking is a decade behind companies like Amazon - yet it's the lifeblood of the economy. Our customers deserve better tools, and only now, with AI, are they possible to build. If you want to build the brains of the next-generation dispatch engine, we'd love to meet you.