Lead Machine Learning Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Lead Machine Learning Engineer based in Canada.

This role sits at the intersection of advanced machine learning engineering, systems architecture, and technical leadership, driving the design and delivery of scalable, production-grade ML solutions. You will lead end-to-end ML initiatives, from early system design and feasibility through deployment, monitoring, and continuous optimization in complex, high-impact environments. The position blends hands-on engineering with strategic influence, requiring you to shape technical direction while remaining deeply engaged in implementation. You will work closely with cross-functional teams and clients to translate business needs into robust ML systems that operate reliably at scale. The environment is highly collaborative, innovation-driven, and focused on engineering excellence and continuous learning. You will also play a key role in mentoring engineers and raising the overall technical bar across teams.

• Lead the design and development of scalable machine learning systems and end-to-end ML pipelines, ensuring reliability, performance, and maintainability in production environments.
• Define technical strategy for ML initiatives, contributing to architectural decisions, program inception, and alignment with broader business and client objectives.
• Own the full lifecycle of ML solutions, including model development, deployment, monitoring, evaluation, and iterative improvement based on real-world performance.
• Translate complex client requirements into feasible ML system designs, guiding delivery across distributed, high-stakes projects.
• Drive adoption of MLOps best practices, CI/CD for ML, and modern distributed system patterns across teams.
• Mentor and guide engineers through technical leadership, code reviews, and knowledge sharing, fostering a culture of excellence and continuous improvement.
• Stay current with emerging ML technologies and methodologies, proactively introducing innovations that enhance system capability and impact.

Requirements:

• 10+ years of software engineering experience with strong exposure to machine learning engineering, data science, and production-scale systems.
• Proven experience designing and operating scalable ML systems using frameworks such as Scikit-learn, TensorFlow, PyTorch, and MLFlow or Kubeflow.
• Strong proficiency in Python with expertise in writing clean, maintainable, and testable code.
• Deep understanding of distributed systems, cloud infrastructure (AWS, GCP, Azure), and infrastructure-as-code for ML workloads.
• Hands-on experience building ML pipelines, model training and deployment workflows, and implementing MLOps and CI/CD practices.
• Strong architectural thinking, including system design, scalability patterns, and lifecycle management of ML models.
• Excellent communication and stakeholder management skills, with the ability to influence technical and non-technical audiences.
• Demonstrated leadership ability, including mentoring engineers and driving technical direction in ambiguous environments.
• Experience working with modern ML infrastructure and tools for training, serving, monitoring, and evaluation.

Benefits:

• Competitive salary range aligned with Canadian market benchmarks ($156,000 – $251,000 CAD)
• Equity opportunities as part of a long-term incentive package
• Comprehensive health, dental, and vision insurance coverage
• Flexible work environment with remote-first culture
• Paid time off and holiday closures with generous vacation policies
• Professional development support and continuous learning programs
• Wellness and mental health support programs
• Inclusive, collaborative engineering culture focused on innovation and growth