Staff Machine Learning, Operations Research Engineer

This is a fully remote position, open to applicants in United States, +1 more country.

📋 Description

• Define and develop the core intelligence behind Dispatch OS, Burq's platform for machine learning-assisted dispatch decisions.

• Establish the technical direction for Burq's pricing, selection, forecasting, and delivery routing processes.

• Personally construct high-impact models and optimization systems.

• Own the machine learning and optimization roadmap while making critical technical decisions.

• Design comprehensive architecture for model serving, evaluation, and large-scale optimization.

• Navigate ambiguous and high-stakes modeling and optimization challenges from initial framing to production implementation.

• Direct technical work across both Engineering and Data teams.

• Establish standards for experimentation, evaluation, and production-level machine learning.

• Mentor engineers through design reviews, collaborative coding, and code assessments.

• Collaborate with Product and leadership on product strategy and leverage machine learning/operations research for competitive advantage.

• Design and implement models for quote selection, dynamic pricing, and reliability scoring.

• Develop models for demand and volume forecasting.

• Create solver-based optimization for batching, route optimization, and vehicle/fleet recommendations.

• Utilize large language models and AI agents to enhance dispatch workflows, including provider-rule extraction and quote follow-ups.

• Construct repeatable evaluation frameworks for customer model validation.

• Translate operational constraints into model requirements, scoring logic, and optimization frameworks.

• Design and manage automated MLOps pipelines for training, deployment, monitoring, and retraining.

• Employ AI tools daily to expedite experimentation, evaluation, and debugging.

⛳️ Requirements

• Over 9 years of experience in applied machine learning or ML engineering, including several years at the senior or staff level, with models deployed and maintained in production.

• Proven track record of setting technical direction for machine learning or optimization systems, where your architectural decisions have influenced a product or platform for multiple years.

• Demonstrated capability to lead intricate technical initiatives across teams without direct authority.

• History of ML or optimization systems delivering quantifiable, company-wide business impact (e.g., tens of millions in revenue or significant utilization or margin enhancements), preferably in pricing, logistics, marketplaces, or operations.

• Extensive experience with decision-making, ranking, and scoring challenges where model outputs directly influence business actions.

• Strong quantitative and algorithmic reasoning skills, including combinatorial problems, constraint satisfaction, and algorithm design.

• Hands-on experience in formulating and solving optimization challenges (LP/MIP, constraint programming, or VRP-style routing).

• Experience with time-series forecasting in a production environment.

• Practical experience deploying LLM-based systems in production settings, including fine-tuned models, extraction pipelines, or agents.

• Experience managing end-to-end machine learning pipelines and MLOps (training, deployment, monitoring, retraining).

• Comfortable working with messy, incomplete, and constraint-heavy operational data, able to create models that adhere to strict business constraints rather than treating them as soft penalties.

• Experience developing evaluation frameworks that are understandable and trustworthy for non-technical stakeholders.

• Nice to have: Familiarity 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: Experience in pricing or revenue management in aviation, fleet, or transportation sectors.

• Nice to have: Published work, patents, or open-source contributions in machine learning, operations research, or pricing.

🏝️ Benefits

• Fully remote work environment.

• Comprehensive medical, vision, and dental insurance.

• Reimbursement for educational courses.

• Generous time off policy.