Staff Machine Learning Engineer

Accountabilities • Advance LLM research and implementation: Research, design, and implement state-of-the-art large language model inference and orchestration techniques, translating emerging research into practical, scalable production solutions.

• Optimize model performance and efficiency: Identify opportunities to leverage smaller language models through fine-tuning, distillation, and other optimization techniques to improve performance, reduce operational costs, and meet application requirements.

• Build scalable AI/ML infrastructure: Design, develop, and operate robust machine learning systems and cloud-based services capable of supporting high-volume data processing, streaming workloads, and real-time inference at enterprise scale.

• Establish AI evaluation standards: Develop evaluation frameworks, metrics, and tooling for assessing a wide range of AI/ML capabilities, ensuring that model behavior, output quality, reliability, and performance can be measured consistently.

• Strengthen MLOps and LLMOps practices: Drive the design and implementation of operational foundations for deploying, monitoring, evaluating, and continuously improving machine learning models and LLM-powered applications.

• Develop and evaluate autonomous agents: Contribute to systems that support agentic workflows, including multi-turn interactions, contextual consistency, and reliable behavior across complex tasks.

• Drive technical innovation: Identify and integrate emerging AI development frameworks, tools, and techniques to accelerate engineering workflows and focus development efforts on high-value technical challenges.

• Own complex technical initiatives: Take end-to-end responsibility for ambitious projects, from initial exploration and experimentation through implementation, production deployment, and ongoing optimization.

• Collaborate across teams: Work effectively with engineering colleagues and other stakeholders to align AI/ML platform capabilities with product requirements and broader organizational objectives.

• Share technical expertise: Contribute to engineering excellence through technical documentation, knowledge sharing, research, open-source contributions, and other activities that advance the wider machine learning community.

Requirements

• At least 8 years of applied AI/ML experience, with a demonstrated track record of owning technically ambitious systems end to end and making meaningful architectural and implementation decisions.

• Strong proficiency in Python and experience developing production-grade machine learning applications and services.

• Solid foundations in statistical machine learning, transformer architectures, and large language models, including an understanding of their capabilities, limitations, and evaluation requirements.

• Extensive experience with MLOps and LLMOps patterns, including designing and implementing rigorous evaluation metrics for non-deterministic AI features at scale.

• Proven experience building cloud-based services on AWS, GCP, Azure, or comparable platforms, particularly systems involving high-volume data, streaming pipelines, real-time inference, and diverse data stores.

• Demonstrated ability to operate in early-stage or ambiguous technical environments, define solutions from limited initial requirements, and move projects from concept to production with a strong bias for action.

• Strong problem-solving and analytical skills, with the ability to investigate complex technical challenges and make sound engineering decisions.

• Excellent communication and collaboration skills, including the ability to explain complex concepts, influence technical direction, and work effectively with teammates across functions.

• Familiarity with AI-assisted development tools such as Claude, Codex, GitHub Copilot, Cursor, or comparable technologies.

• Ability to rapidly learn new technical frameworks, adapt to evolving AI capabilities, and maintain high engineering standards in a fast-paced environment.

Additional qualifications considered an asset:

• Experience building evaluation frameworks for autonomous agents and large language models.

• Experience developing autonomous agents that support multi-turn conversations and maintain long-term context and consistency.

• Previous experience working with globally distributed engineering teams.

• Evidence of technical leadership and impact through open-source machine learning or data projects, published research papers, technical presentations, or engineering blog posts.

Benefits

• Competitive compensation: Estimated annual salary range of $160,320–$200,400, with a target annual bonus of 12.5%. Final compensation depends on relevant skills, experience, and geographic location.

• Additional compensation opportunities: Depending on role eligibility, access to incentive programs, equity grants, and other compensation components.

• Comprehensive healthcare: Health and wellness benefits designed to support employees' physical and mental well-being, with specific offerings varying by location.

• Retirement savings: Access to a retirement savings program, with applicable contributions and plan details determined by location and eligibility.

• Generous paid time off: Vacation, personal time, and other paid leave options, with specific entitlements varying by location.

• Parental and wellness leave: Dedicated leave programs that support family responsibilities, personal well-being, and work-life balance.

• Remote-first work environment: Work remotely from eligible locations in Ontario, British Columbia, or Alberta, Canada, with occasional travel for team gatherings, project meetings, or customer engagements as required.

• Professional growth: Opportunities to work with advanced AI/ML technologies, explore emerging research, and develop expertise in production-scale intelligent systems.

• Collaborative global culture: Join a distributed team that values inclusion, knowledge sharing, innovation, and meaningful technical contributions.

• Community engagement: Opportunities to support volunteering, charitable donations, and positive change within local communities.

How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Why Apply Through Jobgether?

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