Senior Software Engineer, Applied AI

About the role

Justworks is hiring a Senior Software Engineer focused on Applied AI to join their team in Toronto, Canada. This role involves building and deploying machine learning models that power core products and internal tools across the organization. The engineer will work closely with cross-functional teams to identify opportunities where artificial intelligence can solve real business problems and improve the experience for customers and employees alike. The position requires a strong foundation in software engineering principles combined with hands-on expertise in applying AI techniques to practical, production-grade systems.

Key facts

What you'll do

Design and implement machine learning pipelines that transform raw data into actionable insights for business stakeholders across multiple departments.

Build and maintain applied AI models that integrate directly into Justworks products and internal workflows used by teams throughout the company.

Collaborate with product managers and data scientists to define problem statements, set evaluation criteria, and measure model performance in production environments.

Write clean, well-documented code in Python and relevant frameworks to support scalable AI applications that meet high reliability and maintainability standards.

Conduct rigorous experiments to compare different modeling approaches and recommend the best solutions based on quantitative results and observed business impact.

Monitor deployed models for accuracy drift and performance degradation, implementing automated retraining strategies and alerting mechanisms as needed over time.

Partner with engineering teams to containerize and deploy models using cloud infrastructure, orchestration tools, and CI/CD practices for continuous delivery.

Contribute to the development of data infrastructure that supports feature engineering, data validation, and model training at scale across the organization.

Mentor junior engineers and share knowledge about best practices in machine learning operations, software design patterns, and code quality standards.

Participate actively in code reviews and architectural discussions to ensure alignment with team standards and long-term technical direction and goals.

Translate business requirements into technical specifications and work with stakeholders to prioritize AI initiatives based on impact and feasibility.

Stay current with the latest research and developments in applied AI and evaluate how new techniques can be adopted responsibly within the team.

Requirements

Bachelor's degree in computer science, engineering, mathematics, or a related technical field, or equivalent practical experience gained through self-study or professional projects.

5 or more years of professional software engineering experience building production systems that are reliable, maintainable, and performant under real-world conditions.

Strong proficiency in Python and hands-on experience with machine learning libraries such as TensorFlow, PyTorch, or scikit-learn in practical projects.

Solid understanding of data structures, algorithms, and object-oriented design principles applied to building complex and scalable software systems.

Experience working with relational databases and non-relational stores to store, query, and retrieve training and inference data efficiently and reliably.

Familiarity with cloud platforms such as AWS, GCP, or Azure for deploying, scaling, and managing machine learning workloads in production.

Comfortable working in an agile development environment with iterative delivery, frequent releases, and close collaboration with cross-functional teams.

Excellent written and verbal communication skills and the ability to explain technical concepts clearly to non-technical stakeholders and business partners.

Proven track record of taking machine learning models from prototype stage through to production deployment and ongoing monitoring and maintenance.

Ability to work independently on complex problems while also collaborating effectively within a team-oriented engineering culture and environment.

Nice to have

Experience with large language models or generative AI techniques applied to practical business use cases and customer-facing features.

Background in human resources technology, payroll processing, or workforce management platforms and the challenges they present to engineering teams.

Knowledge of MLOps tools and practices for model versioning, monitoring, lineage tracking, and lifecycle management in production settings.

Contributions to open-source machine learning projects or published research in applied AI and related academic or industry disciplines.

Familiarity with data governance, privacy regulations, and ethical considerations when deploying AI systems that handle sensitive or personal information.

Experience with A/B testing frameworks and statistical methods for validating the impact of AI-driven features on key business metrics.

Skills & tools

Python programming language for data analysis, model development, and building production AI applications at scale.

Machine learning frameworks including TensorFlow, PyTorch, or scikit-learn for training, evaluating, and iterating on models efficiently.

Cloud services such as AWS, Google Cloud Platform, or Microsoft Azure for infrastructure provisioning and model deployment.

SQL and NoSQL databases for storing structured and unstructured data used in training and inference pipelines reliably.

Docker and Kubernetes for containerization, orchestration, and scalable serving of machine learning models in production environments.

Git and CI/CD pipelines for version control, automated testing, and continuous integration of AI code changes and updates.

Data processing libraries such as Pandas, NumPy, or Spark for transforming and preparing datasets for model training and evaluation.

REST APIs and microservices architecture for integrating AI capabilities into larger software systems and existing products.

Practical notes

This role is based in Toronto, Canada, with a requirement to work on-site or in a hybrid arrangement as determined by the company's current workplace policies.

Justworks is an equal opportunity employer and welcomes applicants from all backgrounds, identities, and experiences to apply for this position.

The hiring process may include multiple technical interviews, coding assessments, and collaborative discussions with members of the applied AI team at Justworks.

Candidates should be prepared to discuss past projects where they successfully deployed machine learning models in production environments and the measurable outcomes achieved.

The team values diversity of thought and encourages candidates to share unique perspectives and approaches to problem-solving during the interview process.

Offer timelines and next steps will be communicated by the recruiting team after each stage of the evaluation process is completed and reviewed.