Machine Learning Resident - Client: Blue Raven (1 year term)

“If you are interested in the application of machine learning and signal processing for real-time fraud detection, this is the right opportunity for you. You will be part of a team of research and machine learning scientists building production-ready fraud detection systems from the ground up and will receive mentorship from experienced researchers and practitioners in the field. ” - Mara Cairo, Product Owner, Advanced Technology

Description

About the Role

This is a paid residency that will be undertaken over a 12-month period with the intention to be hired by our client, Blue Raven, afterwards (note: at the discretion of the client). The Resident will report to an Amii Scientist and regularly consult with the client team to share insights and engage in knowledge transfer activities. Successful candidates will be members of a cross-functional project team with backgrounds in ML research, project management, software engineering, and new product development. This is a rare opportunity to be mentored by world-class scientists and to develop something truly impactful.

About the Client

Blue Raven is a well-funded Canadian startup operating in stealth mode and focused on applying advanced AI techniques to the detection and prevention of financial fraud. The company is building an AI-native platform designed to operate in real-time, with a strong emphasis on accuracy, reliability, and continuous learning. Blue Raven has validated market demand and developed a working proof of concept, and is now transitioning from early technical validation to scaled product development. The team is intentionally small, highly technical, and focused on building durable IP at the intersection of applied machine learning and real-world fraud prevention.

Blue Raven’s founding team has a track record of building successful startups from the ground up. Brian Heath , a 3-time founder with

multiple successful exits and 13 patents, brings deep expertise in building products that scale.  Rob Fraser , our technical leader, brings 26 years of hands-on development experience and over a decade of engineering leadership.

About the Project

This project focuses on advancing an AI-driven financial fraud detection system by transforming an existing decision-management framework into a continuously learning model. The work emphasizes improving classification accuracy, reducing false positives and false negatives, and designing learning algorithms that adapt over time with human oversight. The project will involve assessing and prioritizing AI modeling opportunities, improving data labeling and evaluation practices, and documenting scalable approaches to learning-based fraud detection across audio and text modalities. The outcome is a robust, production-oriented learning system that balances accuracy, performance, and reliability while remaining adaptable to evolving fraud patterns.

Required Skills / Expertise

Are you passionate about building great solutions? You’ll be presented with opportunities to both personally and professionally develop as you build your career. We’re looking for a talented and enthusiastic individual with a solid background in machine learning, particularly in building systems that combine audio signals and text transcripts to detect fraud in real time. You will help extend an existing fraud detection system by implementing agentic workflows, improving false-positive evaluation and mitigation, and enabling the system to learn over time with human oversight.

Key Responsibilities:

• Design, implement, optimize, and evaluate models for real-time fraud detection using audio and text data.


• Prepare, curate, and preprocess high-quality datasets for training or fine-tuning, and validating models.


• Utilize state-of-the-art machine learning techniques and ML frameworks, tools and open-source libraries to enhance model performance, accelerate workflows, and optimize data processing.


• Undertake applied research on ML and audio processing techniques to address the limitations in existing models.


• Optimize ML pipelines to ensure efficiency, scalability, and real-time processing capabilities.


• Collaborate with the project team and stakeholders to develop MVP and client focused solutions.


• Engage in regular client meetings, contributing to presentations and reports on project progress.

Required Qualifications: 

• Completion of a Computer Science (or a related graduate degree program) MSc. or PhD, ideally with specialization in audio/speech processing, LLMs, or fraud/anomaly detection.


• Proficient in developing and training, fine-tuning and evaluating classical machine learning as well as deep neural network models.


• Proficient in Python programming language and related ML frameworks, libraries, and toolkits (e.g., Scikit-learn, TensorFlow, PyTorch, OpenCV, Pandas, HuggingFace).


• Solid understanding of classical statistics and its application in model validation.


• Familiarity with Linux, Git version control, and writing clean code.


• A positive attitude towards learning and understanding a new applied domain .


• Must be legally eligible to work in Canada.

Preferred Qualifications:

• Familiarity with and hands-on experience with audio data.


• Publication record in peer-reviewed academic conferences or relevant journals in machine learning.


• Experience/familiarity with software engineering best practices.


• Experience with deploying machine learning models in production environments or strong software engineering (or MLE) skills is a plus.

Non-Technical Requirements: 

• Demonstrated ownership of complex problems and strong leadership skills.


• Interdisciplinary team player enthusiastic about working together to achieve excellence.


• Capable of critical and independent thought.


• Able to communicate technical concepts clearly and advise on the application of machine intelligence.


• Intellectual curiosity and the desire to learn new things, techniques, and technologies.

Why You Should Apply

Besides gaining industry experience, additional perks include:

• Work under the mentorship of an Amii Scientist for the duration of the project


• Participate in professional development activities


• Gain access to the Amii community and events


• Get paid for your work (a fair and equitable rate of pay will be negotiated at the time of offer)


• Build your professional network


• The opportunity for an ongoing machine learning role at the client’s organization at the end of the term (at the client’s discretion)

About Amii

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.

How to Apply

If this sounds like the opportunity you've been waiting for, please don’t wait for the closing January 27, 2025 to apply - we’re excited to add a new member to the Amii team for this role, and the posting may come down sooner than the closing date if we find the right candidate before the posting closes! 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.