Senior Platform Engineer building internal developer platforms, cloud infrastructure, and automated delivery pipelines. Managing containers, observability, reliability, security, and governance.
About the role
- Platform Engineer developing innovative machine-learning and optimization systems for supply chain applications. Collaborating on infrastructure supporting data analysis and model deployment in a global organization.
Responsibilities
- Investigate novel techniques combining class leading heuristics with optimization and ML
- Translate real world Supply Chain Management use cases into mathematical models
- Lead the design and implementation of mathematical models and ML systems
- Define test strategies and develop comprehensive test plans
- Write unit testing, integration testing, and debugging to ensure robust and error-free software
- Design, develop, and maintain automated test scripts for functional, regression, and performance testing using testing frameworks and tools
- Collaborate closely with your agile team members and other stakeholders
Requirements
- MSc or PhD in Computer Science, Machine Learning, Operations Research, Engineering, or related field
- 3+ year of software development experience, track record of delivering commercial software
- Working knowledge of C++, including object-oriented design and design patterns, unit testing
- Experience building and maintaining distributed services and frameworks in C++ and Python
- Experience deploying and operating ML or optimization workloads in cloud or containerized environments
- A love of data structures and algorithms, and the desire to apply them in the real world
- Working knowledge of mathematical optimization and mixed-integer programming concepts
- Familiarity with commercial optimization solvers (Gurobi, Xpress, CPLEX) and their application in production systems
- Ability to find opportunities to accelerate the SDLC through innovative application of AI or other tooling, while upholding architecture consistency, secure design, and code-quality standards
- Ability to review AI-generated code rigorously for correctness, architectural fit, integration risk, and edge case support with a growth mindset and bias for experimentation
- Familiarity with GPU-accelerated computing frameworks, distributed optimization systems, or high-performance computing environments is highly desirable
Benefits
- Flexible vacation and Kinaxis Days (company-wide days off)
- Flexible work options
- Physical and mental well-being programs
- Regularly scheduled virtual fitness classes
- Mentorship programs, training, and career development
- Recognition programs and referral rewards
- Hackathons
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