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Burq is revolutionizing logistics and commerce with an AI-powered dispatch platform. The company is backed by top-tier Silicon Valley investors (Village Global, whose LPs include Bill Gates, Jeff Bezos, Mark Zuckerberg, Reid Hoffman, and Sara Blakely) and has been recognized as a Fast Company Best Workplace for Innovators and 2025 Inc. Magazine Power Partner.
You will serve as Staff ML/OR Engineer, owning the technical direction and architecture for machine learning and optimization systems at the core of Dispatch OS, Burq's platform for ML-assisted dispatch decisions. This is a hands-on individual contributor role with company-level impact on revenue, margin, and delivery reliability.
Key responsibilities include:
**Technical Leadership & Architecture**
- Own the ML and optimization roadmap for Dispatch OS, determining which problems warrant ML, solver-based optimization, or heuristic approaches
- Design end-to-end architecture for model serving, evaluation, and optimization that scales with order volume, new providers, and customers
- Lead the most ambiguous, high-stakes modeling and optimization problems from framing through production
- Set standards for experimentation, evaluation, and production ML; mentor engineers through design reviews and code review
- Partner with Product and leadership to identify where ML and OR create competitive advantage
**Core Systems & Modeling**
- Quote selection and pricing: design and ship models for quote selection, dynamic pricing, and reliability scoring to improve margin, win rate, and conversion
- Forecasting: build demand and volume forecasting models, including deep learning approaches, to help operations and customers plan 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
- LLMs and agents: apply LLMs and AI agents to dispatch workflows such as extracting provider rules from documents and automating quote follow-ups
- Evaluation and trust: build rigorous, replayable evaluation frameworks that let customers verify model performance before deployment
- Constraint translation: turn operational constraints (driver availability, provider rules, cost ceilings) into model requirements and optimization formulations
- MLOps: design and own automated pipelines for training, deployment, and monitoring at scale
- Use AI tools daily to speed up experimentation, automate evaluation, and debug faster
**Requirements**
- 9+ years in applied ML or ML engineering, including multiple years at senior or staff level, with models shipped to and maintained in production
- Track record of setting technical direction for ML or optimization systems where architecture decisions shaped a product or platform over multiple years
- 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 (e.g., tens of millions in revenue, or major utilization/margin gains), ideally in pricing, logistics, marketplaces, or operations
- Deep experience with decision, ranking, and scoring problems where model outputs directly drive business actions
- Strong quantitative and algorithmic reasoning, including combinatorial problems, constraint satisfaction, and algorithm design
- Hands-on experience formulating and solving optimization problems (LP/MIP, constraint programming, VRP-style routing)
- Experience with time-series forecasting in production
- Hands-on experience deploying LLM-based systems in production (fine-tuned models, extraction pipelines, agents)
- Experience owning end-to-end ML pipelines and MLOps (training, deployment, monitoring, retraining)
- Comfortable with messy, incomplete, constraint-heavy operational data; able to build models that honor hard business constraints
- 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 (OR-Tools, Gurobi, CPLEX)
- Pricing or revenue management experience in aviation, fleet, or transportation
- Published work, patents, or open-source contributions in ML, OR, or pricing
