Senior Software Engineer, Optimization & Forecasting

**Candidate must have their permanent home in the province of Quebec.**
About WorkAxle
WorkAxle builds workforce management technology for complex, highly regulated environments. Our platform helps mid-market and enterprise organizations forecast labour demand, plan their workforce, and build schedules that balance operational needs, employee requirements, contracts, and regulations.
We work at the intersection of software engineering, data, forecasting, and mathematical optimization, building systems that must be scalable, reliable, and explainable in real-world operations.
About the Role
You will work on two closely connected problems at the core of workforce management: forecasting how much labour is needed and determining how that labour should be scheduled.
Scheduling is a large-scale combinatorial problem involving thousands of employees, multiple locations, and constraints such as skills, availability, seniority, rest requirements, union agreements, and labour regulations. Solutions must balance quality, feasibility, and performance, often under tight latency requirements.
Forecasting provides the demand signal behind these decisions. You will help build models that accurately capture future workload and connect those predictions to downstream planning and scheduling.
As a Senior Software Engineer, you will design and implement optimization and forecasting solutions, improve their performance and reliability, and bring them into scalable production systems. You will work closely with the technical lead and engineers across the platform while contributing to model design, architecture, and technical decisions.
What You Will Do
Optimization & Scheduling
Translate operational, contractual, and regulatory requirements into mathematical models, constraints, and objectives.
Design and implement workforce scheduling and optimization algorithms in collaboration with the technical lead.
Apply constraint programming, mixed-integer programming, heuristics, metaheuristics, and hybrid approaches where appropriate.
Develop custom algorithms when general-purpose solvers cannot meet scalability or latency requirements.
Improve solution quality, runtime, robustness, and schedule stability on large problem instances.
Diagnose infeasibility and build mechanisms that make optimization results understandable to planners.
Build reusable abstractions for defining and extending workforce constraints and objectives.
Forecasting
Develop forecasting models that translate historical and contextual data into labour-demand signals.
Evaluate models using temporal backtesting and appropriate accuracy, bias, and stability metrics.
Apply time-series and machine-learning approaches, including relevant operational and external covariates.
Analyze forecast uncertainty and its impact on workforce planning and scheduling.
Monitor model performance and investigate drift, degradation, and unexpected production behaviour.
Production Engineering
Take optimization and forecasting models from prototype through production deployment and ongoing improvement.
Build automated correctness, regression, performance, and scalability tests.
Develop benchmarks and realistic workload scenarios to evaluate algorithmic changes.
Add observability and instrumentation to diagnose model behaviour and performance in production.
Integrate with APIs, data pipelines, asynchronous workflows, and adjacent platform services.
Build and operate containerized workloads using AWS, Kubernetes, and CI/CD pipelines.
Design for failure handling, time limits, reproducibility, scalability, and operational resilience.
Technical Collaboration
Contribute to architecture and technical decisions across the optimization and forecasting stack.
Evaluate new algorithms and technologies against real-world business and performance requirements.
Work with product, engineering, and domain experts to translate ambiguous problems into technical solutions.
Communicate modeling assumptions, trade-offs, and results clearly to technical and non-technical stakeholders.
Participate in design and code reviews and maintain high standards for correctness and maintainability.
What We Are Looking For
We are looking for strong algorithmic problem-solving ability combined with the engineering discipline required to operate mathematical systems in production.
5+ years of relevant experience building production software, optimization systems, forecasting systems, or other algorithmic applications.
Strong proficiency in at least one of Python, Java, or C++, with the ability to work across languages when needed.
Hands-on experience with combinatorial optimization, including constraint programming, MIP, heuristics, metaheuristics, or related techniques.
Experience with solvers such as OR-Tools / CP-SAT, Gurobi, CPLEX, SCIP, Xpress, or similar technologies.
Strong fundamentals in algorithms, data structures, object-oriented design, software architecture, and computational complexity.
Experience with SQL and relational data models, including performance considerations at scale.
Experience with AWS or a comparable cloud platform, Kubernetes, containerization, and CI/CD.
Background in statistics, machine learning, or quantitative modeling sufficient to work effectively with forecasting systems.
Strong analytical, debugging, and communication skills.
Nice to Have
Master's degree or PhD in Operations Research, Computer Science, Industrial Engineering, Data Science, Applied Mathematics, or a related field.
Experience with workforce scheduling, rostering, resource allocation, logistics, or capacity planning.
Production experience with time-series or demand forecasting.
Experience with advanced optimization techniques such as decomposition, large-neighbourhood search, or local search.
Experience with simulation or discrete-event simulation.
Experience with real-time optimization, incremental schedule repair, or large-scale decision systems.
Familiarity with production machine-learning workflows, model monitoring, and experimentation.

Work arrangement
Yes