“If you are excited about applying to LLMs to tackle real-world challenges in real estate appraisal and land-use analysis, this is a perfect opportunity for you. Be a part of the team of research and machine learning scientists building deployable real-world ML applications from ground up and get mentored by some of the best minds in AI during the process.”
- Soumik Farhan, Machine Learning Scientist and Amor Provins, Product Owner, Advanced Technology
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, ModeDi, 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
ModeDi Inc. is a Manitoba-based Canadian technology company devoted to modernizing appraisal professional’s collection, management, and analysis of real estate data. The company’s mission is to allow appraisers and real estate professionals to do better work, faster, so that their focus can remain on valuation, client advice, and professional judgment rather than repetitive administrative tasks. Its vision is to deliver personalized, secure, and accessible real estate data solutions that provide users with a modernized workflow effectively regardless of firm size.
About The Project
The development is a real estate-focused artificial intelligence LLM that will combine the firm's internal company data and publicly available data to support more informed property analysis for appraisers. The tool provides the background information of each conclusion to the user with an evaluated confidence level for background benefits. The planned capability is intended to help appraisal professionals identify relevant property information, analyze comparable and historical data, and develop stronger valuation insights while keeping the data used secure and the final conclusions with the appraiser.
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, large language models, and agentic AI systems, along with proven experience in applied settings.
Key Responsibilities
- Design, build, and evaluate a secure LLM-based system that streamlines appraisal workflows and supports highest and best use (HBU) analysis for real estate appraisers.
- Develop ingestion and retrieval pipelines that parse mixed text and tabular content from municipal planning documents and zoning bylaws.
- Build citation-grounded generation and confidence estimation components so that every output can be traced to its source documents and used to defend completed appraisal reports.
- Build evaluation infrastructure, including expert-annotated benchmarks, citation accuracy and faithfulness scoring, confidence calibration analysis, adversarial and prompt-injection test suites, and CI-gated regression testing.
- Implement secure system infrastructure with role-based access control that restricts access exclusively to vetted appraisers, along with auditable logging of system usage.
- Conduct applied research in retrieval-augmented generation, document and table understanding, and uncertainty quantification to improve the accuracy and reliability of system outputs.
- 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 with specialization in Natural Language Processing, Machine Learning, Large Language Models, or Applied AI.
- Research or project experience with retrieval-augmented generation, document parsing, or LLM-based question answering over long or semi-structured documents.
- Proficiency in Python and modern AI frameworks such as PyTorch, Hugging Face, Transformers, vLLM, Unsloth, LangGraph, Pydantic, and commercial LLM APIs (e.g., Anthropic, OpenAI), and related machine learning libraries.
- 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
- Experience designing evaluation frameworks for LLM or retrieval-augmented systems, including benchmark construction, LLM-as-judge methods, faithfulness and citation evaluation, and offline and online evaluation.
- Experience with the Model Context Protocol (MCP), agent orchestration frameworks, or durable multi-step workflow systems.
- Familiarity with prompt injection, guardrails, and the safe handling of untrusted input in production LLM systems.
- Experience with uncertainty quantification or confidence calibration for LLM outputs.
- Exposure to real estate, appraisal, urban planning, or another regulated domain where auditability, source traceability, and access control are first-class requirements.
- Experience with deploying machine learning models in production environments or strong software engineering (or MLE) skills is a plus.
- Publication record in peer-reviewed academic conferences or relevant journals in ML or Applied AI (especially in natural language processing or LLM systems).
Non-Technical Requirements
- 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 19, 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.
