[Hiring] Staff Machine Learning / Operations Research Engineer @Burq, Inc.

Role Description

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 experimentation, automate eval pipelines, and debug faster.

Qualifications

• 9+ years in applied ML or ML engineering, including multiple years operating at the 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 you made 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 or margin gains), ideally in pricing, logistics, marketplaces, or operations.
• Deep experience with decision, ranking, and scoring problems where model outputs directly drive a business action.
• 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, or VRP-style routing).
• Experience with time-series forecasting in production.
• Hands-on experience deploying LLM-based systems in production, such as fine-tuned models, extraction pipelines, or agents.
• Experience 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 penalties.
• 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.

Requirements

• Fully remote
• Medical, vision, and dental insurance
• Reimbursement for educational courses
• Generous time off