Software Development Manager, Leo Security

About the role

Amazon Leo is a constellation of Low Earth Orbit satellites that will provide low‑latency, high‑speed broadband network connectivity to unserved and underserved communities around the world. We are looking for an experienced software development manager to lead the Engineering and R&D team within Leo Infrastructure and IP Security. The team defends the manufacturing lines, launch sites, and global ground infrastructure behind the constellation from the most sophisticated threat actors on the planet. You will grow and lead a multidisciplinary team of software engineers, applied scientists, and security engineers building a neuro‑symbolic reasoning platform: large language model agents combined with a knowledge graph and real‑time asset state store that ground every conclusion in verifiable fact, so every automated decision is auditable. The platform automates incident response, threat analysis, offensive security testing, and detection authoring, backed by a high‑throughput streaming pipeline that takes a detection from concept to production in hours.

Export control requirement: Candidates must be a U.S. citizen.

Key responsibilities

• Own the full software development lifecycle for the platform, including architecture and design reviews, roadmap planning with science and security peers, code review, operational excellence, on‑call and live‑site response, and hiring plans.
• Act as a player/coach: set technical direction, own delivery across every workstream, recruit and develop the team, stay hands‑on with code and data, lead designs, unblock investigations, and review the hardest changes.
• Set the bar on model alignment, evaluation, and interaction patterns that make automated decisions trustworthy at scale.
• Identify opportunities where automation can replace manual security operations while maintaining rigorous controls.

A day in the life

• Review agent and detection designs before production to ensure new capabilities enforce guardrails, produce auditable decisions, and degrade safely when models are wrong.
• Evaluate incoming threat intelligence with the team and reshape priorities when adversary capabilities expose coverage gaps.
• Obsess over the two latencies that define the platform—the time from event to detection and the time from detection to containment action—and drive continuous improvement.
• Drive engineers and scientists through sprint execution, make trade‑off decisions between research ambition and delivery velocity, and coordinate with security operations teams to reflect real workflows.
• Collaborate closely with principal engineers and partner security teams on integration points such as telemetry ingestion, detection deployment, and automated response.
• Maintain a production mandate: the platform runs continuously across a global site portfolio and executes actions with real operational consequences.

About the team

Leo Infrastructure and IP Security protects the people, facilities, hardware, and supply chain behind a global satellite constellation. The Engineering and R&D team builds the platforms and tooling the security pillar teams operate on, moving security operations from manual triage to correlation‑based detection, automated response, and agentic AI. The team is composed of applied scientists, software engineers, and security engineers working across physical and digital security domains.

Inclusive team culture

In Amazon Security, it’s in our nature to learn and be curious. Ongoing DEI events and learning experiences inspire us to continue learning and to embrace our uniqueness. Addressing the toughest security challenges requires that we seek out and celebrate a diversity of ideas, perspectives, and voices.

Training & career growth

We continuously raise our performance bar as we strive to become Earth’s Best Employer. You’ll find endless knowledge‑sharing, training, and other career‑advancing resources to help you develop into a better‑rounded professional.

Work / life balance

We value work‑life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.

Basic qualifications

• 3+ years of engineering team management experience
• 7+ years of engineering experience
• 8+ years of leading the definition and development of multi‑tier web services
• Bachelor’s degree or foreign equivalent in Computer Science, Engineering, Mathematics, or a related field
• 5+ years of designing, building, and operating distributed systems, including data pipelines, event‑driven architectures, or stream processing systems
• Experience leading and influencing your team or organization, or experience developing and deploying LLMs in production on GPUs, Neuron, TPU, or other AI acceleration hardware
• Experience aligning research, engineering, and operational delivery across science and engineering peers
• Proficiency in at least one of Python, Rust, or Java, with the ability to read, review, and write production code on the team’s stack

Preferred qualifications

• Experience communicating with users, other technical teams, and senior leadership to collect requirements, describe software product features, technical designs, and product strategy
• Experience recruiting, hiring, mentoring/coaching, and managing teams of Software Engineers to improve their skills and effectiveness
• Experience communicating results to senior leadership, or building financial and operational reports/data sets that inform business decision‑making
• Experience leading teams that operate graph databases or knowledge‑graph platforms (Neptune, Neo4j, or equivalent) at production scale
• Experience leading teams that build real‑time event correlation or stream processing systems at terabyte scale
• Experience building human‑in‑the‑loop approval workflows and staged rollout systems for AI‑driven automation in environments where wrong actions have material operational consequences
• Experience designing observability and instrumentation systems for production ML or AI workloads, including trace collection, evaluation harnesses, and cost and latency monitoring for LLM‑based systems