Job Description
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Company Overview
A leading technology and logistics organization operates a large-scale fulfillment network that uses machine learning, optimization, and systems engineering to improve delivery quality, operational efficiency, customer experience, merchant outcomes, and profitability. Its Fulfillment Planning team develops intelligent systems that support critical logistics decisions across the delivery lifecycle.
The team works on real-time assignment, fulfillment estimation, routing, batching, demand shaping, specialized delivery planning, and other logistics optimization capabilities. These systems operate at significant scale and require high reliability, low latency, and strong operational discipline.
The organization is seeking a Staff Machine Learning Engineer to lead the design, development, and deployment of large-scale production machine learning systems that power real-time decision-making across the fulfillment ecosystem.
The role will initially focus on ML systems supporting assignment and fulfillment estimation while offering opportunities to contribute to batching, fulfillment execution, demand shaping, and logistics optimization. This is a high-impact individual contributor position requiring Staff-level technical leadership, architectural ownership, and cross-functional influence.
Key Responsibilities
- Own and build foundational machine learning systems that directly impact delivery quality, cost, and overall logistics efficiency.
- Solve complex real-world machine learning problems involving real-time assignment, routing, and fulfillment estimation.
- Lead 0→1 machine learning initiatives and define how ML and optimization techniques are applied across fulfillment products.
- Influence architecture, strategy, and execution for highly critical logistics services.
- Collaborate closely with Product, Data Science, Engineering, and Platform teams to deliver scalable ML solutions.
- Establish best practices for model development, deployment, monitoring, retraining, and governance.
- Define and advance the organization’s AI vision for logistics, including the development of foundation-model-inspired intelligence systems.
- Design scalable architectures and technical standards for logistics machine learning.
- Mentor engineers and help raise the technical bar across the machine learning organization.
- Develop reusable ML systems and modeling patterns that can scale across the broader logistics ecosystem.
- Lead the end-to-end development and operation of production machine learning systems.
- Ensure high reliability, low latency, operational excellence, and appropriate governance for mission-critical ML services.
Required Qualifications
- 8+ years of industry experience building and deploying production-scale machine learning systems.
- Strong fundamentals in machine learning and the ability to apply them to large-scale production environments.
- Fluency in Python.
- Hands-on experience with modern machine learning frameworks, particularly deep learning frameworks.
- Experience designing, launching, and operating mission-critical machine learning models or systems in production.
- Experience with production ML monitoring, retraining, reliability, and governance.
- Ability to lead complex technical projects from conception through production.
- Strong ability to influence stakeholders across multiple teams or organizations.
- Excellent communication skills with both technical and non-technical audiences.
- Ability to operate effectively in ambiguous problem spaces and transform 0→1 concepts into production systems.
- Experience building or deploying large-scale machine learning models for areas such as recommendation systems, advertising, marketplaces, logistics, or related domains.
- Experience applying knowledge distillation techniques to transfer capabilities from large teacher models into efficient production models.
Preferred Experience
- Experience working with real-time decisioning systems.
- Experience with logistics, routing, assignment, marketplace optimization, or fulfillment systems.
- Experience designing Tier-0 or other mission-critical machine learning services.
- Experience establishing organization-wide ML engineering standards and best practices.
- Experience mentoring engineers and providing technical leadership across multiple teams.
- Experience applying advanced AI and foundation-model approaches to large-scale operational systems.
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