Staff Machine Learning / Operations Research Engineer

Burq as a Staff Machine Learning / Operations Research Engineer. In this hands-on role, you will define and build the intelligence at the core of Dispatch OS, our platform for ML-assisted dispatch decisions. You will set the technical direction for pricing, selection, forecasting, and routing deliveries, and personally build the highest-leverage models and optimization systems. You will own the architecture and roadmap for ML and optimization across the platform, make key technical decisions, and raise the bar for how we build, evaluate, and ship models.

Responsibilities

• This role has a significant impact on revenue, margin, and delivery reliability.
• Define and build the intelligence at the core of Dispatch OS, including pricing, selection, forecasting, and routing.
• Set the technical direction for ML and optimization across the platform, making key technical decisions and raising the bar for model building.
• Lead complex technical initiatives across teams, ensuring successful deployment and maintenance of ML models in production.
• Experience owning end-to-end ML pipelines and MLOps (training, deployment, monitoring, retraining)
• Experience building evaluation frameworks that non-technical stakeholders can understand and trust

Requirements

• Demonstrated ability to lead complex technical initiatives across teams without direct authority
• 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
• Deep experience with decision, ranking, and scoring problems where model outputs directly drive a business action

Experience with time-series forecasting in production

• 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
• 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
• Strong quantitative and algorithmic reasoning, including combinatorial problems, constraint satisfaction, and algorithm design
• Track record of setting technical direction for ML or optimization systems, where architecture decisions you made shaped a product or platform over multiple years

Preferred

• Hands-on experience deploying LLM-based systems in production, such as fine-tuned models, extraction pipelines, or agents
• Hands-on experience formulating and solving optimization problems (LP/MIP, constraint programming, or VRP-style routing)
• 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