Job Description

"I'm incredibly excited to welcome a new Machine Learning Engineer to our team! This role is perfect for someone passionate about diving deep into optimizing AI workflows, HPC, and shaping the future of our infrastructure. We're looking for a collaborative individual who thrives on both technical excellence and guiding others, ultimately making a significant impact on our research productivity and the advancement of state-of-the-art ML models. I can't wait to see the innovative solutions you'll bring to Amii!"



- Greg Burlet, Director of Engineering

About Amii

Alberta Machine Intelligence Institute (Amii) is one of Canada's three main institutes for artificial intelligence (AI) and machine learning, our world-renowned researchers drive fundamental and applied research at the University of Alberta (and other academic institutions), training some of the world's top scientific talent. Our cross-functional teams work collaboratively with Alberta-based businesses and organizations to build AI capacity and translate scientific advancement into industry adoption and economic impact.

About the Role

The Machine Learning (ML) Engineer plays a key role in ensuring machine learning research and applied AI projects operate securely and effectively. As a key member of the team, the ML Engineer will collaborate with senior engineering leaders to help manage infrastructure, optimize AI workflows, develop training materials, and contribute to the technical development of both individuals and the organization.



The ML Engineer will work with cross-functional teams and external partners to support the development and execution of research and applied projects. Focusing on implementing and managing High-Performance Computing (HPC) systems and pipelines, ensuring efficient allocation of resources such as GPUs and TPUs, and providing support for users of these systems. This role is critical to advancing research productivity and enabling state-of-the-art machine learning models.



In addition to hands-on technical work, the ML Engineer will contribute to the strategic planning of our infrastructure, working alongside the Director, IT, and the Director, Engineering to develop strategies, playbooks, and best practices for optimizing our tools, frameworks, and services.



The role focuses on achieving excellence in three main accountabilities:


Infrastructure and Systems Management AI Workflow Optimization Technical Coaching and Collaboration

Required Skills / Expertise

Key Responsibilities:

Assists in the design, implementation, and management of High-Performance Computing (HPC) environments to support machine learning and AI research Oversees computing resources (e.g. GPUs, TPUs, cloud instances) to ensure secure and efficient operations, with a focus on optimizing resource utilization and availability for AI workflows Establishes monitoring and logging systems to track infrastructure performance, proactively detect anomalies, and ensure real-time alerts for system integrity and uptime Implements and maintains automated AI pipelines to streamline model development and ensure effective use of computational resources Provides support by diagnosing and resolving technical issues, performing routine system maintenance, and enhancing the performance of supporting infrastructures Monitors, assesses and analyzes data from ML projects to ensure effective model performance and project outcomes Utilize strong analytical skills to visualize data, analyze statistical trends, and assess the effectiveness of AI software outputs Assists in the development and refinement of playbooks and strategies to maximize the use of tools, infrastructure, libraries, and frameworks Apply hands-on experience with AI tools and frameworks (e.g. TensorFlow, PyTorch, Keras) to support deep learning initiatives Leverage strong knowledge of Linux-based systems (e.g. Red Hat, CentOS, Ubuntu), proficiency in scripting languages (e.g. Bash, Python), Infrastructure as Code (e.g. terraform, opentofu, ansible), container orchestration systems (e.g., Kubernetes), and github workflows to automate tasks and optimize infrastructure performance Identifies and resolves end-user queries related to AI workflows, providing configuration support for software coding issues and infrastructure setups Install, configure, and diagnose software applications for machine learning algorithms and GPU-based computing to ensure seamless operation Collaborates with university system administrators, AI researchers, and support staff to facilitate the integration and operation of AI/ML systems Participates in training, code reviews, and coaching to enhance team members' technical capabilities Designs and delivers technical training on AI/ML workflows, guiding researchers on how to optimize their workflows and make the most of available infrastructure resources Partners with the Director, Engineering and external partners (e.g. academic institutions and industry partners) to support ML projects

Qualifications:

Post Secondary Degree in Computer Science, Information Technology, Data Science, or a related field Advanced Degrees or Certifications in High-Performance Computing (HPC), Computer Science, ML/AI or Cloud Infrastructure (nice to have) 3+ years of experience in ML engineering practice or research in a fast-paced, innovative environment. 5+ years of experience in Python programming, MLOps (e.g., PyTorch, TensorFlow, distributed LLM pre-training), systems administration, or software development roles in UNIX/Linux or large-scale high-performance computing environments (experience working with GPUs preferred), as well as associated cloud computing platform Source Code Analysis: Proficiency in analyzing the source code of AI-based software Experience in academic or industry research environments Familiarity with containerization technologies (e.g. Docker) and orchestration tools (e.g. Kubernetes) Experience with optimizing large-scale AI models for resource efficiency

Attributes and Expectations:

Advanced critical thinking and problem-solving skills Strong interpersonal communication skills for effective collaboration with internal teams and external stakeholders. Operates with integrity and trust Passion for designing effective learning exercises, technical concept illustrations, training, and evaluation tools Ability to manage multiple competing priorities in a fast-paced environment Demonstrates professional maturity, resourcefulness, and self-discipline in pursuing organizational goals A curious mindset with a keen interest in testing new educational tools and technologies Shows presence, self-confidence, sound judgment, and superior problem-solving abilities Fosters high-level cross-functional collaboration and establishes credibility while influencing peers to achieve common goals

What you'll love about us

A professional yet casual work environment that encourages the growth and development of your skills. Participate in professional development activities Gain access to the Amii community and events A chance to learn from amazing teammates who support one another to succeed. Competitive compensation, including paid time off and flexible health benefits. A modern office located in downtown Edmonton, Alberta.

How to Apply

We're excited to add a new member to the Amii team for this role! We are keeping the door open to new applicants until we find the right fit. When sending your application, please send your

resume and cover letter

indicating why you think you'd be a fit for Amii. In your cover letter, please include one professional accomplishment you are most proud of and why.

Applicants must be legally eligible to work in Canada at the time of application.

Amii is an equal opportunity employer and values a diverse workforce. We encourage applications from all qualified individuals without regard to ethnicity, religion, gender identity, sexual orientation, age or disability. Accommodations for disability-related needs throughout the recruitment and selection process are available upon request. Any information provided by you for accommodations will be kept confidential and won't be used in the selection process.