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Role Description

This role offers a unique opportunity to lead the development of next-generation machine learning systems for real-time dispatch and logistics optimization. You will design and implement production-grade ML pipelines, optimization models, and decision-making services that directly impact operational efficiency and customer satisfaction. The position combines hands-on technical engineering with team leadership, mentoring, and cross-functional collaboration across Product, Operations, and Data Engineering teams. You will have ownership over both modeling and deployment, ensuring robust, scalable, and automated ML solutions. The environment is dynamic and data-driven, providing room to experiment, simulate, and iterate on complex supply-demand and service optimization problems. This fully remote role within the U.S. allows you to drive innovation in a high-impact operational setting.

• Architect and deploy end-to-end Python-based ML services for batch and streaming workflows that integrate predictive models into real-time dispatch decisions.
• Build, extend, and validate optimization and machine learning models (e.g., gradient-boosting, deep learning, OR-Tools) to balance service-level objectives and operational costs.
• Simulate and quantify short- and long-term trade-offs in time-horizon dispatch scenarios.
• Operationalize model training, validation, A/B testing, and monitoring using cloud-native tools such as SageMaker and Airflow.
• Mentor and guide a small squad of ML engineers, ensuring high-quality code and robust workflows.
• Collaborate cross-functionally with Product, Operations, and Data Engineering teams to align ML solutions with business objectives.
• Continuously instrument performance metrics, identify failure modes, and implement improvements to optimize NPS, cost efficiency, and overall system performance.

Qualifications

• 6+ years of experience in machine learning engineering with ownership of production ML systems.
• Expert-level Python programming skills and experience designing cloud-native ML pipelines (AWS preferred).
• Hands-on experience with optimization techniques (Mixed Integer Programming, Linear or Stochastic Optimization) and modern ML frameworks (XGBoost, PyTorch).
• Strong SQL skills, feature-store design knowledge, and a strong data-quality mindset.
• Proven ability to translate complex business requirements into mathematically rigorous experiments and ML solutions.
• Experience designing, deploying, and monitoring scalable ML models and services in production.
• Nice-to-have: experience in dispatch, logistics, supply-demand marketplaces, Monte-Carlo simulations, multi-agent systems, hierarchical reinforcement learning, or balancing short-term vs. long-term KPIs.

Requirements

• 6+ years of experience in machine learning engineering with ownership of production ML systems.
• Expert-level Python programming skills and experience designing cloud-native ML pipelines (AWS preferred).
• Hands-on experience with optimization techniques (Mixed Integer Programming, Linear or Stochastic Optimization) and modern ML frameworks (XGBoost, PyTorch).
• Strong SQL skills, feature-store design knowledge, and a strong data-quality mindset.
• Proven ability to translate complex business requirements into mathematically rigorous experiments and ML solutions.
• Experience designing, deploying, and monitoring scalable ML models and services in production.
• Nice-to-have: experience in dispatch, logistics, supply-demand marketplaces, Monte-Carlo simulations, multi-agent systems, hierarchical reinforcement learning, or balancing short-term vs. long-term KPIs.

Benefits

• Competitive base salary range: $150,000–$200,000 USD, based on experience and location.
• Fully U.S.-based remote work with flexible hours.
• Comprehensive health, dental, vision, disability, and life insurance plans.
• 401(k) retirement plan with company match.
• Flexible time off, paid sick leave, and 10+ paid holidays annually.
• Parental and family support benefits.
• Bonus and incentive programs.
• Opportunities for professional growth, mentorship, and leadership within a collaborative, inclusive environment.