Senior Applied Scientist, Last Mile Delivery Automation
- Bellevue, WA
- Permanent
- Full-time
- Design and develop advanced machine learning models and algorithms for autonomous vehicle systems
- Lead research initiatives in areas such as computer vision, sensor fusion, behavioral prediction, and path planning
- Transform research concepts into production-ready solutions that meet strict safety and performance requirements
- Develop novel approaches to solve complex technical challenges in autonomous navigation
- Create and implement metrics and evaluation frameworks to measure model performance
- Collaborate with engineering teams to integrate ML solutions into the autonomous vehicle stack
- Publish research findings and represent Amazon at technical conferences
- Mentor junior scientists and contribute to building a culture of scientific excellenceA day in the life
A typical day for a Senior Applied Scientist begins with analyzing experimental results from recent model deployments. They might spend the morning developing new deep learning architectures for sensor fusion or improving prediction models for pedestrian behavior. Mid-day could involve collaborating with engineering teams to optimize model performance and reduce inference latency. Afternoon activities might include designing experiments to validate new approaches, reviewing research papers for potential application, and participating in technical discussions to solve challenging problems in perception or planning. They would regularly engage in code reviews, documentation of methods, and mentoring of team members. The role requires balancing innovative research with practical implementation, ensuring that scientific advances translate into reliable autonomous system capabilities.About the team
The Applied Science team within Amazon's Last Mile Delivery Automation organization focuses on developing cutting-edge machine learning solutions for autonomous systems. We work at the intersection of computer vision, deep learning, robotics, and control systems to create robust and scalable algorithms that enable safe autonomous operation. Our team combines expertise from diverse scientific backgrounds to tackle fundamental challenges in perception, prediction, and decision-making. We collaborate closely with software engineering teams to deploy our solutions across Amazon's autonomous delivery fleet.BASIC QUALIFICATIONS- 3+ years of building machine learning models for business application experience
- PhD, or Master's degree and 6+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learningPREFERRED QUALIFICATIONS- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit for more information. If the country/region you're applying in isn't listed, please contact your Recruiting Partner.Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $150,400/year in our lowest geographic market up to $260,000/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit . This position will remain posted until filled. Applicants should apply via our internal or external career site.