Staff Machine Learning / Operations Research Engineer

Burq, Inc.

14h•Remote

About The Position

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.

Requirements

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 Haves

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

Responsibilities

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
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