Lead Machine Learning Engineer
New
CanadaFull-TimeLead
Salary156,000 - 251,000 CAD per year
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Job Details
- Experience
- 10+ years
- Required Skills
- AWSPythonGCPMachine LearningPyTorchAzureTensorflowCI/CDMLOps
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.
Responsibilities
- 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.
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