Machine Learning Resident - Client: Trufla (12 month term)

“Join our team to build a cutting-edge, intelligent platform powering tomorrow's insurance ecosystem. If you are an AI/ML researcher or engineer looking to apply your skills to address high-stake business problems with strict liability constraints, we want to talk to you. You will have the opportunity to innovate on systems that combine agentic AI and predictive modeling, all while receiving direct mentorship from leading minds in AI at Amii.”

- Xu Han, Machine Learning Scientist and Amor Provins, Product Owner, Advanced Technology

Description

About the Role

This is a paid Residency that will be undertaken over a twelve-month period with the potential to be hired by our client, Trufla Technology, afterwards (note: at the discretion of the client). The Resident will report to an Amii Scientist and regularly consult and work with our partners 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

Trufla Technology is an award-winning, Calgary-based InsurTech company that builds the core operating software for property and casualty insurance brokers. Trufla was founded in 2018 by Sherif Gemayel, a former broker who built and sold Sharp Insurance, one of Canada’s fastest-growing brokerages. The company exists to give independent brokers the speed, automation, and intelligence of direct-to-consumer insurers without losing the human advice that defines the broker relationship.

Trufla’s connected ecosystem covers the full brokerage workflow: truMarket, a cloud-native broker operating platform for quoting, binding, renewals, and client management; truWeb and truMobile, client-facing digital experiences; DataHub, a centralized data warehouse; and production machine learning systems such as Retention X-Ray (predictive churn) and Coverage X-Ray (cross-sell intelligence). More than 100 brokerages across North America run on Trufla technology, including some of the ten largest global brokers, and quotes that take 45 to 60 minutes on legacy systems complete in under 10 minutes on Trufla’s platform. Trufla has been named Canada’s Best InsurTech Provider in 2022, 2024, and 2025.

Trufla is Canadian-owned, with leadership, research and development, and intellectual property anchored in Calgary. The team brings together former brokers, software engineers, and data scientists who work side by side, so research ideas are tested against real brokerage operations from day one.

About the Project

Insurance brokers work in a highly regulated environment. Every carrier underwriting rule, coverage wording, and provincial regulatory change (such as the current Ontario and Alberta auto insurance reforms) has to be understood, applied correctly, and documented by a licensed broker, often while a client is waiting. That knowledge arrives as unstructured PDFs, bulletins, and emails, and it changes constantly. Today’s tools force a trade-off: rules engines are auditable but brittle and must be re-coded by hand every time a rule changes, while LLM-based approaches are flexible but probabilistic, which is not defensible when a licensed broker carries errors and omissions liability for the advice given.

This project investigates whether that trade-off can be broken. The goal is a hybrid deterministic-adaptive architecture in which agentic AI and retrieval-augmented reasoning are provably bounded by a deterministic rule layer, so that no output can contradict a current carrier or regulatory rule, and every recommendation can be traced back to its source. The work is genuine applied research: the outcomes are uncertain, and a rigorous answer about what works, what doesn’t, and where the limits are is a valuable result in itself. The Resident will contribute to several connected workstreams:

• Insurance knowledge extraction from unstructured documents
• Constrained, event-driven agentic reasoning
• Real-time performance and human-AI trust
• Predictive modeling of market trends

Successful outcomes are designed to feed directly into Trufla’s platform, giving the research a clear path from experiment to real-world impact.

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 AI and machine learning, with a focus on LLM and Agentic AI. 

Key Responsibilities:

• Develop, train, and evaluate AI agents specifically tailored for platform operations automation and predictive modeling.
• Develop event-driven AI agents triggered by platform actions (such as risk profile creation or modification).
• Architect and optimize Retrieval-Augmented Generation (RAG) pipelines.
• Ensure the pipelines work within business guardrails that are highly deterministic.
• Build predictive models that capture market trends.
• Conduct applied research on multi agent architectures to coordinate and deploy AI agents.
• Collaborate with project team and stakeholders to develop minimum viable products (MVPs) and client focused solutions
• Engage in regular client meetings, standups, prepare presentations and reports on project progress.
• Optimize ML pipelines to ensure efficiency, scalability, and real-time processing capabilities.

Required Qualifications: 

• Completion of a graduate level program or higher (M.S./Ph.D) in Computer Science, Artificial Intelligence, Data Science, Engineering, Physics, or related. 
• Research or project experience in ML and generative AI to solve real world business problems that involve noisy datasets.
• Expertise in LLM, RAG, and AI agents, along with familiarity with knowledge graphs.
• Solid understanding of tool augmented reasoning, uncertainty management, and evaluation methodologies of agentic AI systems.
• Familiarity with both open source and closed source AI models and AI coding agents.
• Experience with document AI, OCR, and information extraction for document ingestion.
• Experience with predictive modeling using statistics and classical ML, e.g. XGBoost, SVM, linear regression.
• Proficient in Python programming language and related ML frameworks, libraries and toolkits (e.g. PyTorch, LangChain, LlamaIndex, Hugging Face, Scikit-learn, Pandas).
• Familiarity with Linux, Git version control, containerization, CI/CD, and cloud computing.
• Must be legally eligible to work in Canada.

Preferred Qualifications:

• Experience building and deploying production-level AI systems with rigorous evaluation and latency control.
• Publication record in peer-reviewed academic conferences or relevant journals in machine learning.
• Experience with AIOps and MLOps tools, as well as LLM-as-a-judge for model evaluation.
• Hands-on experience with graph-based techniques/tools for LLM tasks, e.g. GraphRAG, knowledge graph, graph database, neo4j.
• Advanced proficiency in applying AI coding tools, e.g. Claude Code, Codex.
• Advanced experience in transformers and natural language processing (NLP).
• Familiarity with LLM post-training techniques, e.g. supervised fine-tuning, DPO, GRPO.
• Experience with handling time series, event log, and image datasets. 
• Domain knowledge of the insurance industry is preferred but not required.

Non-Technical Requirements: 

• A positive attitude towards learning and understanding a new applied domain. 
• Desire to take ownership of a problem and demonstrated 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 November 11, 2026 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.