Staff ML/OR Engineer - Remote Dispatch Optimization Leader

Burq, Inc.; United States; Remote; USD 180,000 - 280,000; Full time

Benefits offered by this job

Fully remote

Medical, vision, and dental insurance

Educational reimbursements

Generous time off

Job summary

Burq, Inc. is hiring a Staff ML/OR engineer to define and build the intelligence behind Dispatch OS.

You will own architecture, roadmap, and high-impact modeling for pricing, selection, forecasting, and routing, delivering scalable ML and optimization systems that drive revenue and reliability. You will lead technical direction across ML and optimization, work with Product and leadership, and mentor engineers while building robust evaluation and deployment pipelines.

Qualifications

• Senior ML/OR engineer with production experience.
• Ability to lead cross-team technical initiatives.
• Experience with pricing, logistics, or dispatch systems is a plus.

Responsibilities

• Own the ML and optimization roadmap for Dispatch OS.
• Design end-to-end architecture for model serving, evaluation, and optimization.
• Lead high-stakes modeling and optimization problems across teams.
• Set standards for experimentation, evaluation, and production ML; mentor engineers.
• Partner with Product and leadership to shape strategy and competitive advantage.
• Develop models for quote pricing and reliability scoring; improve margins.

Skills

Lead ML/OR

Optimization

MLOps

Time-series forecasting

LLMs & agents

Constraint programming

Decision/Ranking/Scoring

Python/ML frameworks

Distributed ML systems

Tools

OR-Tools

Gurobi

CPLEX

TensorFlow

PyTorch

Job description

Burq, Inc. is hiring a Staff ML/OR engineer to define and build the intelligence behind Dispatch OS.

You will own architecture, roadmap, and high-impact modeling for pricing, selection, forecasting, and routing, delivering scalable ML and optimization systems that drive revenue and reliability. You will lead technical direction across ML and optimization, work with Product and leadership, and mentor engineers while building robust evaluation and deployment pipelines.