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Added 10 mins ago
Geological Technician
Canadian Natural
Calgary, AB
…data, and deliver maps, reports and technical products that support day-to-day and longer-range…
Added 10 mins ago
MR
Ice Cream Process Engineer
Magnum RFM Ice Cream, Inc. (Selecta Philippines)
ON · Simcoe -Simcoe Plant - GHP
- Full-time
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Alberta Machine Intelligence Institute

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

Edmonton, AB, T5J 3B1, Canada

Full-time

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Job details

Job type: Full-time
Posting language: English
Posted: Oct 30, 2026

Original job description

“If you are passionate about leveraging machine learning and harnessing the capabilities of Large Language Models (LLMs) to create innovative solutions tailored for regulatory and compliance challenges within the legal landscape, then this is the right opportunity for you. Be a part of the team of research and machine learning scientists and get mentored by some of the best minds in AI.”

Maithrreye, Machine Learning Scientist.

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 Ultimarii afterwards (note: at the discretion of the client). The resident will be reporting to an Amii Scientist and regularly consult with the Client team to share insights and engage in knowledge transfer activities. At the end of the engagement, it is expected that the work could be publishable to a respected conference or journal.

About our Client

Ultimarii is a leading technology company based in Calgary, Alberta, Canada, dedicated to accelerating major projects. At Ultimarii, we harness the power of artificial intelligence to create innovative products and services that meet the evolving needs of Project Owners and the consultants that support them. Ultimarii is committed to creating a diverse environment and is proud to be an equal opportunity employer.

About the Project

This project involves researching, evaluating, and benchmarking graph retrieval strategies to enhance our existing hybrid RAG pipeline with a GraphRAG layer. The resident will investigate methodologies for building and populating domain-specific knowledge graphs from unstructured regulatory and legal text - including entity and relation extraction - and design graph retrieval strategies. These graph-augmented approaches will be benchmarked against our baseline hybrid search pipeline using real-world queries and measurable evaluation metrics to improve context quality, enable multi-hop reasoning, and reduce retrieval noise across large document collections. Additionally, the project would see the chosen solution through the production implementation phase for further performance and retrieval quality optimization.

Required Skills / Expertise

Are you passionate about building great solutions? Do you want to drive a high impact early-stage business? 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, specifically in LLMs and applied NLP.

Key Responsibilities:

Experiment and extend an existing hybrid RAG pipeline by integrating a graph knowledge layer to enable deeper, entity-aware generation
Build and populate a domain-specific knowledge graph from unstructured text including entity extraction, relation extraction, and graph schema design
Experiment with and evaluate graph retrieval strategies benchmarking graph-augmented approaches against the baseline pipeline using real queries and measurable metrics
Build, train, and evaluate features for LLM applications working iteratively across design, experimentation, and deployment
Collaborate with the project team and stakeholders to develop MVP and client-focused solutions, undertaking applied research to address limitations in existing models
Participate in regular meetings with the client, preparing presentations and reports on findings and progress

Required Qualifications:

Completion of a graduate level program or higher (M.Sc/Ph.D) in Computing Science, ML or Engineering
Research or project experience in Natural Language Processing, Knowledge Graphs, and Large Language Models
Previous experience with LLM use-cases and RAG architectures
Proficient in Python and related ML frameworks, libraries and toolkits (e.g. Scikit-learn, PyTorch, Pandas, Jupyter, Transformers, HuggingFace Datasets, LangChain, LlamaIndex, Neo4j Python driver, NetworkX, PyTorch Geometric, spaCy)
Experience with graph-based data structures, knowledge representation, or semantic web technologies (RDF, OWL, SPARQL is a bonus)
Familiarity with entity extraction and relation extraction methods (e.g. spaCy, Rebel, GLiNER, LLM-based extraction) and graph database/ libraries (e.g. PyG, DGL, NetworkX, Neo4j)
A positive attitude towards learning and understanding a new applied domain
Must be legally eligible to work in Canada

Preferred Qualifications:

Previous experience applying machine learning in NLP, LLMs, and Generative AI projects including prompt engineering, fine-tuning, or retrieval pipeline optimization
Hands-on experience in AI/ML development projects
Publication record in peer-reviewed academic conferences or relevant journals in machine learning

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
Industry experience in energy, project engineering, environmental or legal studies will be considered an asset.

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
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 13, 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

The resident will experiment with and extend a hybrid RAG pipeline by integrating a graph knowledge layer to enable entity-aware generation. They will also build and populate domain-specific knowledge graphs while benchmarking retrieval strategies against baseline pipelines.

Requirements

Candidates must hold a graduate-level degree (M.Sc or Ph.D) in Computing Science, Machine Learning, or Engineering. Applicants are required to have research or project experience in NLP, Knowledge Graphs, and LLMs, along with proficiency in Python and relevant ML frameworks.

Skills

Machine Learning
Large Language Models
Natural Language Processing
RAG Architectures
Python
Knowledge Graphs
Entity Extraction
Relation Extraction
PyTorch
LangChain
LlamaIndex
Neo4j
NetworkX
spaCy
Prompt Engineering
Graph Retrieval

Benefits

Mentorship from an Amii Scientist
Professional development activities
Access to the Amii community and events
Professional networking opportunities
Potential for ongoing employment

Visa sponsorship

Not detected in the job text

Keywords

Machine Learning
Large Language Models
Natural Language Processing
RAG
Knowledge Graphs
GraphRAG
Python
Scikit-learn
PyTorch
Pandas
Jupyter
Transformers
HuggingFace
LangChain
LlamaIndex
Neo4j
NetworkX
PyTorch Geometric
spaCy
Entity Extraction
Relation Extraction
Semantic Web
RDF
OWL
SPARQL
Prompt Engineering
Fine-tuning
Generative AI
Applied Research
Regulatory Compliance

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