Applied Science Manager, Prime Air

Amazon Amazon · Big Tech · Seattle, WA · Research Science

Applied Science Manager leading a team focused on optimizing drone fleet mission planning and orchestration using ML/RL and geospatial data to maximize deliveries per hour. The role involves path planning, scheduling, system architecture, and cross-functional collaboration, with a focus on turning research into deployed features.

What you'd actually do

  1. Lead & Mentor: Manage a cross-functional team of Applied Scientists and Engineers, fostering a culture of scientific rigor and rapid iteration.
  2. Innovate Path Planning: Leverage ML/RL and heuristic search techniques to develop dynamic path-planning algorithms that navigate complex airspace and weather patterns.
  3. Optimize Orchestration: Drive the development of high-scale scheduling systems that manage battery life, maintenance cycles, and delivery windows to maximize fleet utilization.
  4. Geospatial Mastery: Utilize deep geospatial data (3D maps, urban topology, etc.) to improve situational awareness and mission safety.
  5. System Architecture: Define the long-term technical roadmap for mission orchestration, ensuring our systems are modular, scalable, and resilient.

Skills

Required

  • Experience managing a team of scientists and/or engineers in a production environment.
  • PhD or Master’s degree in Computer Science, Robotics, Operations Research, or a related field.
  • Strong foundation in Geospatial Information Systems (GIS) and spatial data analysis.
  • Proven track record of applying Machine Learning (e.g., Reinforcement Learning, Graph Neural Networks) to optimization problems like path planning or vehicle routing.
  • Experience programming in Java, C++, Python or related language
  • Experience with the Scrum methodology (or similar alternatives) for agile software development

Nice to have

  • Experience with autonomous systems, UAVs, or large-scale logistics networks.
  • Knowledge of combinatorial optimization and real-time scheduling constraints.
  • A knack for turning ambiguous "blue sky" research into deployed, high-impact features.
  • Experience communicating with users, other technical teams, and management to collect requirements, describe software product features, and technical designs
  • Experience hiring and growing top talent
  • Knowledge of engineering practices and patterns for the full software/hardware/networks development life cycle, including coding standards, code reviews, source control management, build processes, testing, certification, and livesite operations

What the JD emphasized

  • Maximize deliveries per hour
  • production-ready
  • high-scale scheduling systems
  • complex airspace and weather patterns
  • dynamic path-planning algorithms
  • real-time scheduling constraints
  • ambiguous "blue sky" research into deployed, high-impact features

Other signals

  • Lead a team of scientists and engineers
  • Develop dynamic path-planning algorithms
  • Optimize scheduling systems
  • Apply ML/RL to optimization problems