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. Join a forward-thinking engineering team building the foundational AI and machine learning capabilities behind next-generation intelligent customer engagement experiences. In this senior technical role, you will shape the architecture and evolution of scalable AI/ML infrastructure, with a solid focus on large language models (LLMs), inference optimization, and AI operations. You will bridge advanced research and practical engineering to deliver reliable, production-ready AI systems that operate at enterprise scale. A key part of your mission will be establishing rigorous evaluation frameworks to ensure AI-powered features meet high standards of quality, consistency, and performance. You will also explore model fine-tuning, distillation, and autonomous agent capabilities to improve efficiency and unlock new opportunities. This remote opportunity is ideal for an experienced machine learning engineer who thrives in ambiguous environments, takes ownership of complex technical challenges, and enjoys driving innovation across distributed teams. 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 teammat

📌 Staff Machine Learning Engineer (Winnipeg)
🏢 Jobgether
📍 Winnipeg