“I’m thrilled to welcome a new Machine Learning Engineer to our team! This role is perfect for someone passionate about driving trust and safety research. We’re looking for someone with an applied mindset to join our diverse, cross-functional team, helping turn research into practical outputs that make an impact. I can’t wait to see the contributions you’ll bring to Amii!”
– Alyssa Lefaivre Škopac, Director of AI Trust and Safety

QUALIFICATIONS:

• Post-graduate degree in Computer Science, Information Technology, Data Science, or a related field
• 3+ years of experience in ML engineering, with a strong track record in researching, designing, implementing, optimizing, and evaluating machine learning (ML) and reinforcement learning (RL) algorithms in both simulated and real-world environments.
• Demonstrated professional proficiency in developing and applying deep learning and RL algorithms using Python and modern machine learning frameworks (e.g., PyTorch, JAX, TensorFlow), with expertise in RL libraries and simulation environments (e.g., Gymnasium, MuJoCo, Stable-Baselines3, Dopamine, PureJaxRL), complemented by strong competency in Git-based version control and Unix/Linux environments.
• Experience working in academic or industry research environments, collaborating closely with research scientists.
• Advanced Degrees or Certifications in High-Performance Computing (HPC), Computer Science, ML/AI or Cloud Infrastructure are an asset
• Proven ability translating research concepts into tools, prototypes, or systems considered for real-world use.
• Experience optimizing ML systems and tools for scalability, reliability, and resource efficiency.
• Source Code Analysis: Proficiency in analyzing the source code of AI-based software
• Interest in or exposure to the Trust & Safety landscape and its practical applications

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.

ABOUT THE ROLE

Reporting to the Director of AI Trust and Safety, the ML Engineer will drive the development and execution of research and applied projects within the Trust and Safety portfolio. This role is pivotal in advancing state-of-the-art trust and safety research and delivering high-impact applied outputs.
As part of a rapidly growing team, the ML Engineer will play a central role in shaping the portfolio. The work will begin with a strong emphasis on research and will evolve toward developing applied tools and outputs as research findings are translated into practical, applied solutions. The ML Engineer will collaborate closely with senior engineering and research leaders to perform research, develop applied outputs, manage infrastructure, create training materials, and support the technical growth of both individuals and the organization.
In addition to hands-on technical work, the ML Engineer will work alongside the Director, Engineering and Director, AI Trust and Safety to develop strategies and best practices for optimizing our tools, frameworks, and services.

The role focuses on achieving excellence in three main accountabilities:

• Infrastructure and Systems Management
• Applied Research and Development
• Technical Coaching and Collaboration

KEY RESPONSIBILITIES:

• Work with the Director, AI Trust and Safety and Applied Research Scientists to scope and define safety-focused project goals that balance foundational research with applied value.
• Perform research activities by implementing experiments, adapting models, and maintaining reproducible, well-documented code.
• Design and deliver training on AI/ML workflows for safety research, helping researchers optimize processes and infrastructure usage. Design and maintain data pipelines and compute environments to support safety-focused research, experimentation, and validation.
• Collaborate with internal teams and external partners to deliver practical tools, frameworks, and other applied outputs with real-world impact.
• Lead the engineering of safety tools and inference systems, ensuring outputs from safety research are secure, reliable, interoperable, and considered for real-world Trust & Safety applications.
• Develop and maintain model evaluation frameworks tailored to Trust & Safety research questions and use cases.
• Document systems, workflows, and best practices to ensure reproducibility, transparency, and effective knowledge sharing.
• Prepare and manage datasets — including curation, preprocessing, and synthetic data generation — to accelerate safety research.
• Collaborate with cross-functional teams to productize safety research outputs, build supporting tooling, and enable adoption by end users.
• Provide technical coaching, mentorship, and code reviews to staff, students, and partners on safety-focused ML development.