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Added 3 mins ago
SH
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…our hiring process. To ensure fairness and transparency, all applications are reviewed and assessed by our dedicated team of recruiters and hiring managers, not by artificial intelligence…
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Added 19 mins ago
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Added 19 mins ago
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Added 19 mins ago
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Amii (Alberta Machine Intelligence Institute)

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

AB, Canada
Edmonton, AB, Canada

Full-time

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Job details

Job type: Full-time
Apply by: Dec 5, 2026
Posting language: English
Posted: Nov 5, 2026

Original job description

“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.

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More about this job

Responsibilities

Design, build, and evaluate a secure LLM system for real estate appraisal workflows, including document ingestion and retrieval, source-grounded generation, confidence estimation, and HBU analysis. Develop evaluation and security infrastructure, conduct applied research, and collaborate with the project team and client through regular meetings, presentations, and knowledge transfer.

Requirements

A completed MSc or PhD in computer science or a related field, specializing in NLP, machine learning, LLMs, or applied AI, is required, along with relevant experience in RAG, document parsing, or LLM question answering. Candidates need Python and modern AI framework proficiency, familiarity with Linux and Git, clear communication and teamwork skills, and legal eligibility to work in Canada.

Skills

Machine Learning
Large Language Models
Agentic AI
Retrieval-Augmented Generation
Natural Language Processing
Python
PyTorch
Hugging Face Transformers
Document Parsing
LLM Evaluation
Confidence Calibration
Prompt Injection Defense
Role-Based Access Control
Secure System Design
Software Engineering
Client Communication

Benefits

Mentorship
Professional Development
Access to the Amii Community and Events
Paid Residency
Professional Networking
Potential Ongoing Employment

Visa sponsorship

Not detected in the job text

Keywords

Machine Learning
Large Language Models
Agentic AI
Retrieval-Augmented Generation
Natural Language Processing
Applied AI
Python
PyTorch
Hugging Face
Transformers
vLLM
Unsloth
LangGraph
Pydantic
Anthropic API
OpenAI API
Document Parsing
Table Understanding
Citation-Grounded Generation
Confidence Estimation
Uncertainty Quantification
LLM Evaluation
LLM-as-Judge
Prompt Injection
Guardrails
Model Context Protocol
Role-Based Access Control
Linux
Git
Real Estate Appraisal

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