Senior Applied Scientist, Scheduling and Optimization

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Senior Applied Scientist, Scheduling and Optimization in Canada.

This role focuses on building and evolving the core optimization and constraint-solving intelligence behind a next-generation scheduling system used by enterprise maintenance teams. You will design and improve advanced scheduling models that transform complex real-world constraints—such as workforce availability, multi-site operations, and production windows—into reliable, executable schedules. Working at the intersection of machine learning, operations research, and product engineering, you will help shape how AI-driven scheduling is delivered through modern GenAI-powered workflows. The environment is highly collaborative, blending product thinking with deep technical problem-solving and continuous iteration with real users. You will partner closely with product, design, and engineering teams to ensure the system produces practical, trusted outcomes in production settings. This is a high-impact role where your work directly influences operational efficiency for large-scale enterprise customers. The team operates in a fast-paced, experimentation-driven culture focused on real-world performance and measurable impact.

• Own and evolve the Python-based constraint optimization engine powering scheduling intelligence, continuously improving its modeling and performance.
• Design and implement advanced scheduling capabilities, including workforce constraints, capacity planning, multi-site coordination, and reactive rescheduling.
• Translate complex real-world operational challenges into robust constraint formulations and optimization models.
• Build APIs and structured interfaces that expose optimization capabilities to GenAI agent workflows and tool-calling systems.
• Collaborate with product and design teams to refine scheduling requirements and ensure solutions align with real user needs.
• Iterate on optimization models using real-world feedback from enterprise customers and production usage.
• Enhance service reliability through improvements in testing, observability, performance tuning, and system robustness.
• Contribute to integrating scheduling intelligence with broader platform systems, including learning from execution data to improve model accuracy.

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Requirements

• 5+ years of professional software engineering experience with strong exposure to optimization, constraint programming, or operations research problems in production environments.
• Hands-on expertise with CP-SAT and familiarity with other optimization approaches such as MILP solvers (Gurobi, CPLEX, HiGHS) or metaheuristics.
• Strong Python engineering skills, including API development, system design, testing, and observability for production services.
• Experience shipping optimization systems used by real users and iterating based on feedback and observed outcomes.
• Solid academic background in Operations Research, Industrial Engineering, Computer Science, or a related quantitative field.
• Strong product mindset with a focus on user outcomes, system usability, and measurable impact.
• Comfort working with ambiguity and collaboratively defining constraint models and system behavior.
• Familiarity with GenAI concepts such as tool calling, structured outputs, or prompt-based system design.
• Strong communication and collaboration skills in cross-functional environments.
• Nice to have: experience with large-scale scheduling systems, workforce planning, logistics, or learning-augmented optimization approaches.

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Benefits

• Competitive salary with meaningful equity opportunities.
• Comprehensive healthcare, dental, and vision coverage.
• Retirement savings plan (401(k) / RRSP depending on location).
• Flexible unlimited PTO policy.
• Remote-friendly and globally distributed work environment.
• Collaborative, merit-driven culture that values impact, ideas, and execution.
• Opportunity to work on cutting-edge AI and optimization systems at scale.