Senior Manager, Software Development, Amazon Shipping

About the Role
Amazons Fulfillment Network (AFN) operates at a scale where few organizations in the world can match. As we expand our external volume footprint the need to optimize network cost allocation with scientific rigor has never been more critical. This is a rare depth-first leadership role at the intersection of applied science and engineering designed for a leader who can drive algorithmic innovation while building and scaling high-performing teams.
As a Senior Software Development Manager for Network Optimization you will own the technical strategy and execution for cost allocation frameworks that directly influence how AFN scales to serve external volume. You will lead a small high-impact team of Software Development Engineers (SDEs) and Applied Scientists and will be expected to operate as a technical peer to your scientists not just a manager of them. This role demands a leader who can engage deeply with optimization models cost allocation theory and large-scale network design while simultaneously driving engineering excellence and organizational clarity.
This is not a generalist engineering management role. We are looking for a leader with a strong applied science background someone who has worked hands-on with operations research combinatorial optimization or network flow problems and who brings that depth into every technical decision design review and hiring bar.


Key job responsibilities
What Youll Do
Own end-to-end technical strategy for network optimization and cost allocation systems that support AFNs external volume growth from problem formulation through production deployment at scale.
Lead and grow a team of SDEs and Applied Scientists setting a high technical bar fostering a culture of scientific rigor and ensuring the team delivers measurable business impact.
Partner with science and product leadership to define the roadmap for cost allocation strategies including shared-cost modeling marginal cost attribution and capacity-aware pricing signals.
Drive architectural decisions for large-scale optimization systems ensuring solutions are robust scalable and operationally sound for Tier 1 workloads.
Translate complex optimization and cost modeling concepts into engineering deliverables bridging the gap between research and production systems.
Engage with senior stakeholders across AFN Finance and Transportation to align on cost allocation frameworks and influence network-level investment decisions.
Establish mechanisms for continuous improvement defining success metrics running experiments and iterating on models to improve cost efficiency and network utilization.
Represent the team in technical and business forums communicating complex tradeoffs clearly to both technical and non-technical audiences.


- Bachelors degree
- 10 years of engineering experience with at least 3 years in an engineering management role leading teams of SDEs and/or Applied Scientists.
- Strong applied science background demonstrated experience working with optimization algorithms operations research network flow models or related quantitative disciplines.
- Proven track record of delivering large-scale production-grade systems that solve complex mathematical or algorithmic problems.
- Experience with cost modeling cost allocation strategies or financial optimization in the context of large-scale logistics supply chain or network systems.

- Masters or PhD in Operations Research Applied Mathematics Computer Science or a related field.
- Hands-on experience with network optimization problems including but not limited to minimum cost flow vehicle routing facility location or capacity planning.
- Prior experience in fulfillment logistics or transportation network optimization at scale.
- Demonstrated ability to lead Applied Scientists as a technical peer reviewing model designs contributing to algorithmic approaches and raising the science bar on the team.
- Experience defining and operationalizing cost allocation frameworks in multi-stakeholder environments.
- Familiarity with large-scale distributed systems and the engineering challenges of deploying optimization models in real-time or near-real-time production environments.

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