Senior Machine Learning Engineer

Accountabilities

• Lead the design and implementation of large-scale, production-grade distributed systems that support AI and machine learning features used by millions of users.
• Shape the longer-term technical vision for AI infrastructure in collaboration with staff and senior staff engineers, translating strategic direction into practical, deliverable initiatives.
• Make architecture decisions that balance scalability, reliability, flexibility, operational simplicity, and cost effectiveness.
• Own production services and pipelines, including operational health, on-call responsibilities, incident response, monitoring, and technical debt management.
• Build infrastructure and engineering interfaces that enable Applied Scientists to safely and reliably transition machine learning and LLM models from research into production.
• Develop and operate scalable data workloads using Python, SQL, and distributed processing technologies such as Spark or Dask.
• Deploy and maintain production systems across AWS and Kubernetes environments, ensuring they meet appropriate reliability and performance standards.
• Integrate production-ready generative AI and large language model capabilities into customer-facing product experiences.
• Improve data usability and engineering practices across the AI Products organization, reducing operational toil and raising overall technical quality.
• Provide technical leadership and mentorship to engineers, helping raise engineering standards and supporting the development of less-experienced team members.
• Collaborate across engineering, data, science, and product teams to drive technical initiatives, resolve complex problems, and build consensus around architectural decisions.
• Identify and address technical debt, infrastructure risks, and opportunities to improve the scalability and maintainability of the AI technology estate.

Requirements

• 5+ years of experience building and operating production software services at scale, with strong proficiency in Python or an equivalent programming language.
• Strong software engineering fundamentals, including system design, architecture, coding, testing, debugging, and production operations.
• Proven experience owning production services or data pipelines, including operational or on-call responsibilities, incident response, and long-term technical debt management.
• Deep understanding of distributed processing principles and practical experience with Spark, Dask, or comparable distributed computing technologies.
• Strong SQL capabilities and experience working with large-scale data workloads.
• Demonstrated experience integrating machine learning models or LLM-based capabilities into production systems, with the ability to work effectively alongside Applied Scientists or ML researchers.
• Production experience with AWS and Kubernetes, including deploying and operating cloud-native workloads.
• Familiarity with machine learning technologies such as MLFlow, TensorFlow, or PyTorch and data orchestration tools such as Airflow or Prefect is advantageous.
• Prior experience applying or fine-tuning LLMs in a product environment is a plus, but strong production engineering expertise remains the primary requirement.
• Strong technical leadership skills, with the ability to set direction, make sound architectural decisions, mentor engineers, and raise engineering standards.
• Excellent communication and collaboration skills, particularly when working across multidisciplinary teams and translating complex technical concepts into practical decisions.
• A proactive, pragmatic approach to problem solving, with the ability to navigate ambiguity and drive meaningful technical outcomes.
• Willingness to participate in an on-call rotation and take ownership of the reliability of production systems.
• A growth-oriented mindset, curiosity about emerging AI technologies, and enthusiasm for applying new approaches responsibly in production environments.

Benefits

• Annual base salary range of CA$185,000–CA$225,000, with individual compensation determined by factors such as geography, experience, skills, and role level.
• Eligibility for annual performance bonuses and equity through RSU programs for permanent employees.
• Potential access to additional performance-based cash or equity incentives depending on role level and company performance.
• Comprehensive health, wellness, and retirement programs.
• Wellbeing days and generous paid leave.
• Dedicated professional development budgets to support ongoing learning and career growth.
• Flexible hybrid working model combining remote autonomy with access to modern office spaces in Toronto.
• Collaborative “boost days” designed to support team connection, knowledge sharing, and effective delivery.
• Opportunity to work on AI-powered products and production infrastructure serving millions of users.
• A multidisciplinary environment bringing together engineers, Applied Scientists, product managers, analysts, and data specialists.
• A culture that values skills, impact, curiosity, continuous learning, and diverse perspectives.
• Inclusive and accessible workplace practices, with support and reasonable accommodations available throughout the hiring process.