Architect, Machine Learning (Principal Scientist)
Kinaxis Inc.5 months ago
Remote, CanadaStaff+
Responsibilities
- Identify promising machine learning and AI techniques and develop differentiated approaches for complex supply chain problems.
- Build prototypes, proof-of-concepts, and early systems that demonstrate the value of new ML and AI methods.
- Guide major technical decisions and help translate research ideas into future enterprise product capabilities.
- Partner with product and engineering teams to move promising ideas toward scalable, maintainable software.
- Provide broad technical leadership across teams while remaining hands-on in applied research and innovation.
- Mentor colleagues and foster a culture of rigorous, practical innovation.
- Evaluate emerging AI technologies critically and identify high-leverage opportunities for customer and product impact.
- Review AI-generated code for correctness, architectural fit, integration risk, edge-case support, secure design, and code quality.
Requirements
- PhD in Computer Science, Machine Learning, Artificial Intelligence, Operations Research, or a related field.
- Extensive experience applying machine learning to complex real-world problems and developing or adapting novel approaches.
- Strong hands-on experience building prototypes, proof-of-concepts, and early AI or ML systems.
- Deep expertise in modern AI techniques, including areas such as agentic systems, LLMs, RAG, recommendation, optimization, explainability, and language-based AI.
- Strong programming ability in Python and experience with modern ML and data tooling.
- Demonstrated ability to influence technical direction across teams and collaborate with product and engineering organizations.
- Excellent communication skills with technical and non-technical stakeholders.
- Practical, product-minded approach to enterprise software quality, maintainability, security, and customer impact.
- Ability to accelerate software development through innovative applications of AI or other tooling while maintaining architectural consistency and code-quality standards.
- Preferred experience in supply chain, retail, life sciences, planning, or optimization domains.
- Preferred strong mathematical foundation in probability, statistics, linear algebra, optimization, or stochastic methods.
- Preferred experience with learning from human feedback and AI systems incorporating feedback, oversight, or interaction.
- Preferred record of intellectual property contributions through patents, publications, inventions, or other differentiated technical work.
- Preferred experience turning ambiguous customer or product problems into research directions, prototypes, and validated solution concepts.
- Preferred familiarity with enterprise SaaS products and production AI considerations at scale.
Benefits
- Hybrid work in Ottawa or Toronto, Canada, and remote work from other Canadian and USA locations.
- Flexible vacation and company-wide Kinaxis Days.
- Flexible work options.
- Physical and mental well-being programs and regularly scheduled virtual fitness classes.
- Mentorship, training, and career development programs.
- Recognition programs, referral rewards, and hackathons.
- Accessibility accommodations are available throughout the recruitment process.
Tech Stack
Categories