Description
Do you want to create intelligent, adaptable robots with global impact? The Vulcan Stow team (https://www.amazon.science/latest-news/how-amazon-robotics-researchers-are-solving-a-beautiful-problem) at Amazon Robotics builds high-performance, real-time robotic systems that can perceive, learn, and act intelligently alongside humans—at Amazon scale. We invent and deploy machine learning, optimization algorithms, and geometric reasoning models that empower robots to identify the best available placement opportunities, learn effective stow strategies, and continuously improve capacity utilization.
Our mission is to maximize throughput developing intelligent policies that understand physical constraints, reason about geometric action opportunities, and select behaviors that achieve high-density, reliable stowing. We hire and develop ML experts in reinforcement learning, combinatorial optimization, predictive modeling, and decision systems. Our solutions learn from millions of stowing decisions to continuously improve warehouse capacity and throughput.
We are seeking an experienced Applied Science Manager for Match and Affordances team to lead a team of talented applied scientists and engineers. You will drive ML innovation using the latest advancements in transformer-based architectures to enable maximum storage utilization, learn affordances and behaviors in high-density environments with the reliability and scale that delights our customers. Collaborating with cross-functional teams across perception, motion planning, and fulfillment operations, you will deliver scalable solutions that optimize stow strategy and warehouse capacity across geographies and conditions.
Key job responsibilities
People Leadership: Prioritize being a great people manager - motivating, rewarding, and coaching your diverse team is the most important part of this role. Recruit and retain top talent in machine learning, optimization, and decision systems. Excel in day-to-day people and performance management tasks.
Technical Vision: Set a vision for your team and create technical roadmaps focused on stow policy development, placement optimization, and density improvements. Guide research, design, deployment, and evaluation of ML and RL algorithms, optimization methods, and geometric reasoning systems that inform robot action selection.
Cross-functional Collaboration: Work closely with perception, motion planning, hardware, and fulfillment teams to create integrated solutions that maximize storage density while maintaining operational reliability. Partner with computer vision teams on 3D scene understanding and container geometry representation that drives policy learning.
Delivery Excellence: Implement best practices in applied research and software development. Manage project timelines, resources, and deliverables effectively. Keep technical skills current to contribute meaningfully to architecture and design discussions.
Problem Solving: Regularly participate in deep-dive troubleshooting exercises and drive technical post-mortem discussions to identify root causes of policy regressions, optimization failures, and KPI degradations.
A day in the life
- Prioritize being a great people manager: motivating, rewarding, and coaching your diverse team is the most important part of this role. You will recruit and retain top talent and excel in people and performance management tasks.
- Set a vision for the team and create the technical roadmap that deliver results for customers while thinking big for future applications.
- Guide the research, design, deployment, and evaluation of complex computer vision and machine learning algorithms for contact-rich, cluttered, real-world manipulation problems.
- Work closely with motion, hardware, and software teams to create integrated robotic solutions that are better than the sum of their parts.
- Implement best practices in applied research and software development, managing project timelines, resources, and deliverables effectively.
Requirements
Basic Qualifications
- PhD, or Master's degree and 4+ years of industry or academic research experience
- 6+ years of applied research experience
- 3+ years of scientists or machine learning engineers management experience
- Experience building machine learning models or developing algorithms for business application
- Experience with deep learning libraries such as PyTorch, TensorFlow, MxNet Research publications in computer vision, deep learning or machine learning at peer-reviewed workshops, conferences or journals
- 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
Preferred Qualifications
- Experience hiring and growing top talent
- Experience in representation learning, and reinforcement learning in robotics systems.
- Familiarity with the developments in the VLAs, VLMs, affordance learning and scene-action understanding
