About the job
As a Software Development Engineer on the AR Sortation Planning team, you will design, develop, and deploy full-stack robotic planning, tracking, and workcell solutions using AWS infrastructure and AI & ML driven sortation optimization that are scalable, reliable, and performant. You will build software solutions to improve sortation at scale, improve associate safety and ergonomics, improve associate job satisfaction, and improve site efficiency.
Responsibilities
Build scalable, flexible, and maintainable software solutions to innovative robotics technology integration problems.
Work with peers of diverse backgrounds and experiences to solve challenging problems in a multi-disciplinary environment.
Take fast-paced, high-octane ownership of end-to-end solutions with a focus on working directly with stakeholders, customers, and teammates.
Maintain, implement, and deploy existing software services in Production with established customer bases, requiring adoption of and adherence to metrics, alarms, and operational excellence standards.
Contribute to team technical standards and best practices.
Analyze and balance trade-offs including - evaluating ML model solutions versus heuristic real-time approaches while considering hardware constraints, latency requirements, as well as dependencies on upstream and downstream services to design optimal end-to-end sortation solutions.
Qualifications
Minimum
4+ years of non-internship professional software development experience
2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
2+ years of full stack development experience
Experience building complex software systems that have been successfully delivered to customers
Experience with full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations
Preferred
Master's degree in computer science or equivalent
5+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
Knowledge of Machine Learning and LLM fundamentals, including transformer architecture, training/inference lifecycles, and optimization techniques