About BurqWe'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 prospect to grow faster than ever while doing the most meaningful work of your career.The roleWe'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 doTechnical leadershipTechnical direction: own the ML and optimization roadmap for Dispatch OS, deciding which problems get ML, which get solvers, which stay heuristic, and in what orderArchitecture: design the end‑to‑end architecture for model serving, evaluation, and optimization so it scales with order volume, new providers, and new customersHardest problems: lead the most ambiguous, high‑stakes modeling and optimization problems from framing through production,



and guide technical work across Engineering and DataRaise the bar: set standards for experimentation, evaluation, and production ML, and mentor engineers through design reviews, pairing, and code reviewStrategic partnership: work with Product and leadership to shape product strategy and identify where ML and OR create competitive advantageCore systemsQuote selection and pricing: design and ship models for quote selection, dynamic pricing, and reliability scoring that measurably improve margin, win rate, and conversionForecasting: 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 capacityOptimization: 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 keepLLMs 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 handlingEvaluation 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 actConstraint translation: turn real operational constraints (driver availability, provider rules, cost ceilings) into model requirements, scoring logic, and optimization formulationsMLOps: design and own automated pipelines for training, deployment, and monitoring so models stay accurate in production at scaleAI‑native work: use AI tools daily to speed up experimentation, automate eval pipelines,



and debug fasterRequirements9+ years in applied ML or ML engineering, including multiple years operating at the senior or staff level, with models shipped to and maintained in productionTrack record of setting technical direction for ML or optimization systems, where architecture decisions you made shaped a product or platform over multiple yearsDemonstrated ability to lead complex technical initiatives across teams without direct authorityTrack record of ML or optimization systems with quantified, company‑level business impact (e.G., tens of millions in revenue, or major utilization or margin gains), ideally in pricing, logistics, marketplaces, or operationsDeep experience with decision, ranking, and scoring problems where model outputs directly drive a business actionStrong quantitative and algorithmic reasoning, including combinatorial problems, constraint satisfaction, and algorithm designHands‑on experience formulating and solving optimization problems (LP/MIP, constraint programming, or VRP‑style routing)Experience with time‑series forecasting in productionHands‑on experience deploying LLM‑based systems in production, such as fine‑tuned models, extraction pipelines, or agentsExperience owning end‑to‑end ML pipelines and MLOps (training, deployment, monitoring, retraining)Comfortable with messy, incomplete, constraint‑heavy operational data, and able to build models that honor hard business constraints rather than treating them as soft penaltiesExperience building evaluation frameworks that non‑technical stakeholders can understand and trustNice to haveExperience with delivery/dispatch software, TMS platforms, or routing systemsProduction experience with commercial or open‑source solvers (OR‑Tools, Gurobi, CPLEX)Pricing or revenue management experience in aviation, fleet, or transportationPublished work, patents, or open‑source contributions in ML, OR, or pricingInvesting in youFully remoteMedical, vision, and dental insuranceReimbursement for educational coursesGenerous time off#J-18808-Ljbffr

📌 Staff Machine Learning / Operations Research Engineer (Winnipeg)
🏢 Tandem
📍 Winnipeg