Machine Learning​/Operations Research Engineer

About Burq

We're revolutionizing commerce from port to doorstep with Burq Intelligence.

Burq started with an ambitious mission: to turn the complex process of offering delivery into a simple, turnkey solution. It's a big mission, and now we want you to join us in making it even bigger.

We're proud to be recognized as one of Fast Company's Best Workplaces for Innovators and a 2025 Inc. Magazine Power Partner, awards that highlight how we're redefining the future of logistics while empowering our partners to grow.

Backed by leading Silicon Valley investors like Village Global, the fund whose investors include Bill Gates, Jeff Bezos, Mark Zuckerberg, Reid Hoffman, and Sara Blakely, we've built a world-class team across the globe.

We operate at scale but remain small enough for every person to have a massive impact. There's a lot of important work ahead, and joining Burq means the opportunity to grow faster than ever while doing the most meaningful work of your career.

The role

We're hiring a Staff ML/OR engineer to define and build the intelligence at the core of Dispatch OS, our platform for ML-assisted dispatch decisions. You'll set the technical direction for how Burq prices, selects, forecasts, and routes deliveries, and you'll personally build the highest-leverage models and optimization systems behind it.

You’ll own the architecture and roadmap for ML and optimization across the platform, make the key technical bets, and raise the bar for how we build, evaluate, and ship models. This is a hands‑on IC role with company‑level impact on revenue, margin, and delivery reliability.

What you’ll do

Technical leadership

• Technical direction: own the ML and optimization roadmap for Dispatch OS, deciding which problems get ML, which get solvers, which stay heuristic, and in what order
• Architecture: design the end‑to‑end architecture for model serving, evaluation, and optimization so it scales with order volume, new providers, and new customers
• Hardest problems: lead the most ambiguous, high‑stakes modeling and optimization problems from framing through production, and guide technical work across Engineering and Data
• Raise the bar: set standards for experimentation, evaluation, and production ML, and mentor engineers through design reviews, pairing, and code review
• Strategic partnership: work with Product and leadership to shape product strategy and identify where ML and OR create competitive advantage

Core systems

• Quote selection and pricing: design and ship models for quote selection, dynamic pricing, and reliability scoring that measurably improve margin, win rate, and conversion
• Forecasting: build demand and volume forecasting models, including modern deep learning approaches where they outperform classical methods, to - help operations and customers plan driver and fleet capacity
• Optimization: move dispatch decisions from heuristics to solver‑based optimization for batching, route optimization, and vehicle/fleet recommendation, using OR‑Tools, Gurobi, or similar where it earns its keep
• LLMs and agents: apply LLMs and AI agents to dispatch workflows, such as extracting provider rules and rate terms from documents, and automating quote follow‑ups and exception handling
• Evaluation and trust: build rigorous, replayable evaluation frameworks that let customers verify a model’s performance against their own historical decisions before they trust it to act
• Constraint translation: turn real operational constraints (driver availability, provider rules, cost ceilings) into model requirements, scoring logic, and optimization formulations
• MLOps: design and own automated pipelines for training, deployment, and monitoring so models stay accurate in production at scale
• AI‑native work: use AI tools daily to speed up…