Engineering Manager, ML & Optimization Systems
Job Description
Team: IT
This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Engineering Manager, ML & Optimization Systems in the United States.
This leadership role sits at the intersection of machine learning, operations research, and large-scale cloud engineering, driving the next generation of real-time dispatch optimization systems. You will lead a high-impact team of Data Scientists, ML Engineers, and Software Engineers focused on building intelligent, low-latency decisioning platforms that directly improve service efficiency and operational outcomes. The role combines hands-on technical leadership with strategic ownership of ML and optimization roadmaps, ensuring research models are transformed into production-grade systems. You will shape architecture for scalable cloud-native services, oversee MLOps pipelines, and define best practices for model deployment, monitoring, and experimentation. Working cross-functionally with Product, Operations, and Engineering, you will translate complex scientific insights into business-critical decisions. This is a highly visible role with direct influence on real-time systems that operate at national scale and impact millions of service events annually.
Accountabilities:
- Lead, mentor, and develop a multidisciplinary team of Data Scientists, ML Engineers, and Software Engineers, fostering a high-performance and collaborative culture.
- Own delivery of the ML and optimization roadmap, including project planning, estimation, risk management, and execution within Agile/Scrum frameworks.
- Define and guide the scientific and algorithmic strategy for constrained optimization and machine learning-driven dispatch systems.
- Architect and oversee end-to-end cloud-native systems enabling real-time decision-making, batch/stream processing, and optimization algorithms.
- Establish and scale MLOps practices, including automated training pipelines, model validation, A/B testing, deployment, and monitoring.
- Drive operational excellence by managing system reliability, technical debt, incident response, and production performance optimization.
- Partner with cross-functional stakeholders to communicate technical trade-offs, operational insights, and strategic recommendations.
- Lead continuous improvement initiatives across platform performance, cost efficiency, and system scalability.
- Bachelor’s degree in Computer Science, Data Science, Operations Research, Engineering, or a related quantitative field (Master’s preferred).
- 6+ years of experience in Data Science, ML Engineering, or Operations Research, with strong experience deploying models into production systems.
- 2+ years of experience in engineering or technical leadership roles managing ML/DS teams.
- Deep expertise in machine learning techniques (e.g., XGBoost, PyTorch, Transformers) and optimization methods (MIP, linear, stochastic optimization).
- Strong experience building real-time, low-latency systems in Python, SQL, and AWS-based cloud environments.
- Proven ability to design and operate MLOps pipelines using tools such as Airflow, SageMaker, or similar platforms.
- Experience with large-scale, 24/7 production systems and incident-driven operational environments.
- Strong background in system architecture, distributed systems, and data-driven decision-making.
- Excellent leadership, communication, and stakeholder management skills with the ability to influence across technical and non-technical teams.
- Experience with BI/analytics tools, data modeling, and emerging AI/ML technologies (e.g., LLMs, generative AI) is highly desirable.
- Competitive compensation package with a national salary range of USD $180,000 – $230,000 per year.
- Comprehensive health, dental, vision, disability, and life insurance coverage.
- 401(k) retirement plan with employer match and tuition assistance programs.
- Flexible work arrangements and generous paid time off, including holidays and sick leave.
- Family support benefits, including parental planning assistance.
- Performance-based bonus and incentive programs.
- Professional development opportunities in advanced AI, ML, and optimization systems.
- Inclusive and collaborative culture focused on innovation, growth, and long-term impact.
- Travel support for onboarding and occasional onsite collaboration or company events.
Requirements:
Benefits:
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Date Posted
05/18/2026
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