Staff 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 Staff Machine Learning Engineer based in Canada.

This is a high-impact opportunity for an experienced machine learning engineer to help build AI-native solutions that are transforming a complex industry. You will design scalable ML and LLM pipelines across multiple use cases, taking systems from experimentation through reliable production deployment. The role combines hands-on engineering with technical leadership, mentorship, and cross-functional collaboration. You will help establish strong standards for reliability, observability, testing, and compliance across ML workloads. Working from first principles, you will solve challenging problems while moving quickly and iterating based on real-world results. This role is ideal for an experienced engineer who wants to shape both the technology and engineering practices of a rapidly growing AI organization.

• Design and implement efficient, scalable machine learning pipelines supporting a range of business and insurance use cases.
• Develop and maintain production-grade infrastructure for both classical machine learning and large language model workloads.
• Build reusable, modular infrastructure components and CI/CD pipelines that enable rapid experimentation and reliable transitions from research to production.
• Establish and maintain standards for scalability, reliability, security, testing, and compliance when deploying ML systems.
• Implement observability and monitoring practices, including model and data drift detection, logging, performance tracking, and automated rollback strategies.
• Deploy and operate machine learning solutions using cloud platforms, APIs, and cloud-based data processing and storage services.
• Serve as a technical leader and mentor for junior engineers, providing guidance that strengthens engineering quality and team capabilities.
• Collaborate with cross-functional teams to translate complex technical challenges into practical, production-ready solutions.
• Balance rapid innovation with operational stability, ensuring ML systems remain reliable and maintainable as they scale.

Requirements

• 8+ years of professional experience as a Machine Learning Engineer or in a closely related engineering role.
• At least 2 years of hands-on experience working extensively with large language models and LLM-based applications.
• Strong expertise in designing robust and scalable machine learning pipelines for both traditional ML and LLM workloads.
• Demonstrated experience automating, deploying, monitoring, and maintaining machine learning workflows in production environments.
• Hands-on experience with cloud platforms, including model deployment, cloud resource management, and APIs for data ingestion, storage, and processing.
• Strong understanding of production ML engineering practices, including scalability, reliability, observability, testing, monitoring, and CI/CD.
• Ability to provide technical leadership, mentor engineers, and establish effective engineering standards.
• Strong communication skills, with the ability to explain sophisticated technical concepts clearly to non-technical stakeholders.
• A first-principles mindset, strong problem-solving abilities, adaptability, and enthusiasm for learning and experimentation.
• Ability to work effectively in a fast-paced environment where priorities evolve and rapid iteration is encouraged.

Benefits

• Annual cash compensation of $210,000–$250,000 , with final compensation determined by factors such as location, experience, and expertise.
• Stock options as part of the total compensation package.
• Comprehensive employee benefits and additional perks.
• Opportunity to work on advanced AI and machine learning challenges with significant real-world impact.
• Exposure to complex LLM and ML systems across an AI-native technology environment.
• Strong opportunities for technical leadership, mentorship, and professional growth.
• A collaborative culture that values first-principles thinking, continuous learning, experimentation, and adaptability.
• Remote work environment with the opportunity to contribute to a rapidly growing global organization.
• Inclusive workplace committed to equal opportunity and providing reasonable accommodations throughout the employment process.