Software Development Engineer, Middle Mile Planning and Optimization

- Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field
- 3+ years of professional software development experience
- 2+ years of professional experience programming in a modern programming language e.g. Java, C++, C#, Python experience
- Experience in computer science fundamentals (object-oriented design, data structures, algorithm design, problem solving and complexity analysis)
- Experience designing, building, operating, and managing large-scale distributed systems or web services
- Master's degree in Computer Science, Operations Research, or related field
- Experience with optimization algorithms, operations research, or machine learning
- Knowledge of AWS services (EC2, S3, Lambda, DynamoDB, etc.)
- Familiarity with transportation, logistics, or supply chain domains
- Strong problem-solving skills and ability to work in ambiguous environments
- Excellent communication and collaboration skills
- Experience with agile development methodologies

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The base salary range for this position is listed below. As a total compensation company, Amazon's package may include other elements such as sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon offers comprehensive benefits including health insurance (medical, dental, vision, prescription, basic life & AD&D insurance), Registered Retirement Savings Plan (RRSP), Deferred Profit Sharing Plan (DPSP), paid time off, and other resources to improve health and well-being. We thank all applicants for their interest, however only those interviewed will be advised as to hiring status.

CAN, BC, Vancouver - 114,800.00 - 191,800.00 CAD annually
The Route Execution Optimization (REO) team in Middle Mile Planning and Optimization (MMPO) is seeking a Software Development Engineer II to build systems that automatically respond to transportation network disruptions—weather delays, facility closures, equipment failures, and driver constraints. You'll design intent-based automation that orchestrates optimal route corrections while maintaining safety, compliance, and efficiency. This role offers the opportunity to solve complex distributed systems challenges at massive scale, affecting hundreds of thousands of loads weekly, directly reducing operational costs and improving driver safety across Amazon's middle-mile transportation network.

Key job responsibilities
- Design, develop, and maintain scalable distributed systems that process millions of transportation events daily
- Build optimization algorithms and machine learning models for forecasting, scheduling, and route planning
- Collaborate with scientists, product managers, and operations teams to translate business requirements into technical solutions
- Write clean, maintainable code with comprehensive testing and documentation
- Participate in code reviews and contribute to engineering best practices
- Mentor junior engineers and contribute to team knowledge sharing
- Investigate and resolve production issues, ensuring high system availability
- Drive continuous improvement in system performance, cost efficiency, and operational metrics

About the team
Middle Mile Planning and Optimization (MMPO) is an integral part of Transportation Tech Services (TTS) , serving as the primary engine for efficiency and cost savings in surface transportation. MMPO optimizes resource allocation, demand forecasting, route planning, and network equipment balancing while minimizing CO2 emissions.

The Route Execution Optimization (REO) team within MMPO focuses on bridging the gap between planning and execution. While MMPO's systems optimize the network days in advance, REO ensures plans remain executable when real-world disruptions occur—weather delays, facility closures, equipment failures, and driver constraints. REO builds intelligent automation that responds to disruptions affecting hundreds of thousands of loads weekly, transforming manual interventions into automated corrections that preserve safety, reduce costs, and enable continuous optimization.