Staff Machine Learning, Operations Research Engineer

Burq

. Define and build the intelligence at the core of Dispatch OS, Burq's platform for ML-assisted dispatch decisions .

Posted 10/8/2026full-timeRemote • United States, CanadaLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive expertise in Machine Learning and optimization systems, with a strong focus on building and deploying end-to-end ML pipelines and MLOps. Proven ability to lead complex technical initiatives and drive significant business impact through data-driven decision-making.

Highest-signal resume keywords

Machine Learning EngineeringOptimization SystemsEnd-to-End ML PipelinesTime-Series ForecastingMLOps

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Model ServingAlgorithm DesignConstraint SatisfactionLinear ProgrammingMixed Integer ProgrammingVehicle Routing ProblemDemand ForecastingDynamic Pricing ModelsEvaluation FrameworksQuantitative Reasoning

Soft Skills

Technical LeadershipMentoringCross-Team CollaborationProblem SolvingCommunication

Tools & Technologies

MLOps ToolsAI ToolsOR-ToolsGurobiCPLEX

Industry Keywords

Dispatch SoftwareTransportation Management SystemsPricing StrategiesLogisticsMarketplaces

About the role

Key responsibilities & impact

Define and build the intelligence at the core of Dispatch OS, Burq's platform for ML-assisted dispatch decisions
Set technical direction for how Burq prices, selects, forecasts, and routes deliveries
Personally build high-leverage models and optimization systems
Own the ML and optimization roadmap and make key technical bets
Design end-to-end architecture for model serving, evaluation, and optimization at scale
Lead ambiguous, high-stakes modeling and optimization problems from framing through production
Guide technical work across Engineering and Data
Set standards for experimentation, evaluation, and production ML
Mentor engineers through design reviews, pairing, and code review
Partner with Product and leadership on product strategy and ML/OR competitive advantage
Design and ship quote-selection, dynamic-pricing, and reliability-scoring models
Build demand and volume forecasting models
Develop solver-based optimization for batching, route optimization, and vehicle/fleet recommendation
Apply LLMs and AI agents to dispatch workflows, including provider-rule extraction and quote follow-ups
Build replayable evaluation frameworks for customer model validation
Translate operational constraints into model requirements, scoring logic, and optimization formulations
Design and own automated MLOps pipelines for training, deployment, monitoring, and retraining
Use AI tools daily to accelerate experimentation, evaluation, and debugging

Requirements

What you’ll need

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
Nice to have: Production experience with commercial or open-source solvers (OR-Tools, Gurobi, CPLEX)
Nice to have: Pricing or revenue management experience in aviation, fleet, or transportation
Nice to have: Published work, patents, or open-source contributions in ML, OR, or pricing

Benefits

Comp & perks

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