Founding Optimisation Engineer in London

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.

About the optimisation team: 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.

What you'll work on:

• 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.

What we’re looking for:

• You think in trade-offs, not ideals. Real fleets don’t hand you a clean objective function.
• You go where the work is. The best modelling decisions in this product come from understanding how the job really gets done.
• You ship, then sharpen. You prototype quickly, get it live, and improve against real results.
• 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.

You may be a good fit if you have:

• 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.

Bonus if you’ve worked on:

• 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.

What we offer:

• 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.

Application process:

• Intro call with the team (30 mins).
• Technical interview (2 hrs).
• 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.