Staff Machine Learning / Operations Research Engineer in Winnipeg

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. 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, 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.

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: 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.
• Optimization: move dispatch decisions from heuristics to solver-based optimization for batching, route optimization, and vehicle/fleet recommendation.
• LLMs and agents: apply LLMs and AI agents to dispatch workflows, such as extracting provider rules and rate terms from documents.
• Evaluation and trust: build rigorous, replayable evaluation frameworks that let customers verify a model’s performance against their own historical decisions.
• Constraint translation: turn real operational constraints 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 experimentation, automate eval pipelines, and debug faster.

Requirements

• 9+ years in applied ML or ML engineering, including multiple years operating at the senior or staff level.
• Track record of setting technical direction for ML or optimization systems.
• Demonstrated ability to lead complex technical initiatives across teams without direct authority.
• Track record of ML or optimization systems with quantified, company-level business impact.
• Deep experience with decision, ranking, and scoring problems.
• Strong quantitative and algorithmic reasoning.
• Hands-on experience formulating and solving optimization problems.
• Experience with time-series forecasting in production.
• Hands-on experience deploying LLM-based systems in production.
• Experience owning end-to-end ML pipelines and MLOps.
• Comfortable with messy, incomplete, constraint-heavy operational data.
• Experience building evaluation frameworks that non-technical stakeholders can understand and trust.

Nice to have

• Experience with delivery/dispatch software, TMS platforms, or routing systems.
• Production experience with commercial or open-source solvers.
• Pricing or revenue management experience in aviation, fleet, or transportation.
• Published work, patents, or open-source contributions in ML, OR, or pricing.

Investing in you
Fully remote. Medical, vision, and dental insurance. Reimbursement for educational courses. Generous time off.