Staff Machine Learning / Operations Research Engineer

New

BurqLogistics technology

Workable workplace: remote; Workable locations: United States. CanadaFull-TimeStaff

Salary not disclosed

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Job Details

Experience: 9+ years in applied ML or ML engineering, including multiple years operating at the senior or staff level
Required Skills: Machine LearningMLOps

Have 9+ years in applied ML or ML engineering, including multiple years operating at the senior or staff level.
Have shipped and maintained models in production.
Have set technical direction for ML or optimization systems with architecture decisions that shaped a product or platform over multiple years.
Demonstrate ability to lead complex technical initiatives across teams without direct authority.
Have delivered ML or optimization systems with quantified company-level business impact.
Have deep experience with decision, ranking, and scoring problems where model outputs drive business actions.
Have strong quantitative and algorithmic reasoning, including combinatorial problems, constraint satisfaction, and algorithm design.
Have hands-on experience formulating and solving optimization problems, such as LP/MIP, constraint programming, or VRP-style routing.
Have production experience with time-series forecasting.
Have hands-on experience deploying LLM-based systems in production.
Have experience owning end-to-end ML pipelines and MLOps, including training, deployment, monitoring, and retraining.
Be comfortable working with incomplete, constraint-heavy operational data and building models that honor hard business constraints.

Own the ML and optimization roadmap for Dispatch OS, deciding which problems use ML, solvers, or heuristics.
Design end-to-end architecture for model serving, evaluation, and optimization.
Lead ambiguous, high-stakes modeling and optimization problems from framing through production.
Set standards for experimentation, evaluation, and production ML, and mentor engineers through design reviews, pairing, and code review.
Partner with Product and leadership on product strategy and opportunities for ML and operations research.
Design and ship quote selection, dynamic pricing, and reliability-scoring models.
Build demand and volume forecasting models to help plan driver and fleet capacity.
Develop solver-based optimization for batching, route optimization, and vehicle or fleet recommendations.
Apply LLMs and AI agents to dispatch workflows, including document extraction, quote follow-ups, and exception handling.
Build evaluation frameworks and automated pipelines for model training, deployment, and monitoring.

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