
Sr. Machine Learning Engineer
- Canada
- Permanent
- Temps-plein
- Lead highly complex ML engineering projects with extensive latitude for independent judgment.
- Design, develop, document, test, and debug software engineering solutions for both customer-facing applications and internal use.
- Deploy, scale, and maintain machine learning models in production environments.
- Develop scalable ML architecture and pipelines.
- Collaborate with data scientists to optimize, test, and evaluate ML models.
- Identify and evaluate new platforms and technologies to enhance our existing systems.
- Follow and help define and enforce our development best practices and standards.
- Provide mentorship and assistance to less experienced team members.
- Collaborate with architects and engineering managers to maintain development roadmaps and prioritize new features.
- Engage with data science and engineering teams to understand requirements and constraints for multiple products.
- Share your technical knowledge and MLOps framework expertise with team members.
- Maintain up-to-date knowledge of related MLOps and data science topics and technologies.
- Serve as the technical expert on the deployment and benchmarking of ML models including LLMs, and generative models.
- Acquire domain expertise in at least one cybersecurity application area.
- Write technical documentation based on architectural design and stated engineering requirements.
- At least 7 years of experience in engineering and/or data science roles is required with at least 1 year of senior role experience included. Multi-year experience as an ML engineer is desirable.
- Strong academic background (M.S or PhD program in a technical field) is an adequate substitute for a few years of industry experience.
- Thorough knowledge of software engineering and data science techniques and methodologies, and experience leading projects applying these methodologies.
- Extensive experience developing system architecture and solving engineering problems in at least one major programming language such as Python, Java, or C++.
- Comprehensive MLOps background and expertise in data processing, model training, and deployment of models as microservices.
- Substantial experience integrating applications with cloud technologies such as AWS.
- Proven track record of deploying and benchmarking ML models in production environments.
- Expertise in MLOps frameworks such as MLflow and Kubeflow is nice to have.
- Solid experience with Python-based frameworks such as PyTorch, TensorFlow, and scikit-learn is a plus.
- Demonstrated ability to collaborate with engineering leads, software architects, and other stakeholders to advance engineering and data science projects.
- Led multiple projects to completion in a small team.
- Proven experience assisting or providing mentorship to junior engineers.
- Excellent communication and presentation skills. Ability to convey complicated technical topics to non-technical people both verbally and in writing.
- Demonstrated excellent problem solving ability and critical thinking skill with a variety of engineering challenges.
- Comfortable and enthusiastic about sharing technical knowledge with team members.
- Interest in cybersecurity applications.