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

Aimlroles; Edmonton; On-site; CAD 60,000 - 70,000; Full time

Benefits offered by this job

Mentorship

Professional development

Community & events

Networking

Future ML role

Job summary

Amii is seeking a motivated resident to advance research on graph-augmented retrieval and LLMs for regulatory and legal text. You will work under a senior Amii Scientist and collaborate with the client team, contributing to knowledge graphs, RAG experiments, and publishable results.

The role emphasizes ML, NLP, entity extraction, and graph databases with opportunities for a full-time position at the client after the residency. Applicants must be eligible to work in Canada.

Qualifications

• Graduate level program or higher (M.Sc/Ph.D) in Computing Science, ML or Engineering.
• Research or project experience in NLP, Knowledge Graphs, and LLMs.
• Experience with LLM use-cases and RAG architectures.

Responsibilities

• Experiment and extend an existing hybrid RAG pipeline by integrating a graph knowledge layer.
• Build and populate a domain-specific knowledge graph from unstructured text.
• Evaluate graph retrieval strategies benchmarking graph-augmented approaches against the baseline pipeline.

Skills

Python

LLMs

NLP

Knowledge Graphs

Entity extraction

Graph retrieval

Education

M.Sc/Ph.D in Computing Science, ML or Engineering

Tools

PyTorch

SpaCy

Neo4j Python driver

LangChain

Transformers

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.

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.