Machine Learning Resident - Client: Mobia Health Innovations (12 month term)

RGIT Australia

Edmonton

On-site

CAD 42,000 - 52,000

Full time

7 days ago
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Benefits offered by this job

Mentorship by Amii Scientist
Amii community access
Competitive pay rate
Professional networking
Potential ongoing role

Job summary

Amii is offering a 12-month Machine Learning Residency focused on applying ML, NLP, and RAG to automate clinical decision support for diagnostic pathways. The project engages with Mobia Health Innovations and CAR guidelines to scale accessNavigator using AI-driven rule extraction and JSON configurations.

Residents will be mentored by Amii scientists, collaborate with a cross-functional team, and may transition to a role with Mobia Health depending on client discretion.

Qualifications

  • PhD or MSc in CS or related field completed
  • Proficient in developing, fine-tuning ML models (PyTorch)
  • Strong understanding of modern transformer architectures and LLMs
  • Proficient in Python and ML libraries (Scikit-learn, PyTorch, Pandas)
  • Hands-on experience with LLM-based systems and retrieval systems
  • Legally eligible to work in Canada

Responsibilities

  • Design, implement, evaluate models for information extraction and data generation
  • Prepare and preprocess high-quality datasets for training or fine-tuning
  • Utilize LLMs, RAG, and ML frameworks to improve performance
  • Conduct applied ML/NLP research to address limitations of models
  • Optimize ML pipelines for throughput, cost, and reproducibility
  • Collaborate with stakeholders to develop MVP and client-focused solutions
  • Lead client discussions and contribute progress reports
  • Act as AI liaison between client and Amii experts

Skills

PyTorch
Python
LLMs
NLP
Machine learning
Linux

Education

MSc or PhD in CS or related

Tools

LangChain
HuggingFace
Pandas

Job description

Machine Learning Resident - Client: Mobia Health Innovations (12 month term)

“If you are interested in the application of machine learning and NLP to improve acute care wait lists through automated clinical decision support, this is the right opportunity for you. Join a team of research and machine learning scientists building AI-driven rule creation pipelines from the ground up and get mentored by some of the best minds in AI during the process.”

About the Role

This is a paid residency that will be undertaken over a 12-month period with the potential to be hired by our client, Mobia Health Innovations, 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

Mobia Health Innovations (a spin-off from MOBIA Technology Innovations) combines the expertise of front-line clinicians and digital transformation specialists to address critical technology gaps in healthcare. On a mission to relieve pressure on the healthcare system, Mobia Health designs and implements innovative Software-as-a-Service (SaaS) solutions such as accessNavigator that optimize clinical workflows, eliminate inappropriate waitlists, and improve patient care delivery

About the Project

Mobia Health, in collaboration with Canadian Association of Radiologists (CAR) and numerous healthcare organizations across Canada have identified that a significant number of diagnostic imaging tests can be avoided if published guidelines are automated into the referral process. This directly reduces challenging wait lists for CT and MRI by ensuring patients receive the most appropriate care.

This project focuses on scaling Mobia Health’s accessNavigator platform to implement guidelines from sources such as CAR by utilizing AI, Machine Learning, and Natural Language Processing to dynamically ingest unstructured healthcare policies and clinical guidelines. In partnership with Amii, the team is building an automated Retrieval-Augmented Generation (RAG) pipeline to extract clinical rules, decision logic, and conditions from evidence-based guidelines—specifically targeting Diagnostic Imaging (MRI and CT scan) referrals and map them into machine-readable JSON platform configurations. By replacing manual setup with automated rule extraction and validation, the project aims to reduce configuration time by 40% to 70%, streamline waitlists, increase clinical referral appropriateness, and provide a graphical user interface for non-technical administrators to manage decision support rules.

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, demonstrated experience with NLP and LLMs, and exposure to healthcare systems or clinical decision-support workflows.

Key Responsibilities:
  • Design, implement, optimize, and evaluate models for information extraction and structured data generation tasks.
  • Prepare, curate, and preprocess high-quality datasets for training or fine-tuning, and validating models.
  • Utilize Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and ML frameworks, tools and open-source libraries to enhance model performance, accelerate workflows, and optimize data processing
  • Undertake applied research on ML and NLP techniques to address the limitations in existing models.
  • Optimize ML pipelines for throughput, cost, and reproducibility across batch document-processing workloads.
  • Implement schema-constrained generation (JSON Schema, Pydantic, function calling or grammar-constrained decoding) together with span-level evidence attribution
  • 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.
  • To provide AI leadership as part of the cross-functional team and act as a liaison between the Client and Amii experts.
Required Qualifications:
  • Completion of a Computer Science (or a related graduate degree program) MSc. or PhD.
  • Proficient in developing, fine-tuning, and evaluating machine learning and deep neural network models in PyTorch.
  • Solid understanding of modern transformer architectures and large language models, including attention, tokenization, decoding strategies, context-window behaviour, and the characteristic failure modes of generative models.
  • Proficient in Python programming language and related ML frameworks, libraries, and toolkits (e.g., Scikit-learn, PyTorch, LangChain, Pandas, HuggingFace).
  • Hands-on experience building LLM-based systems in research or production settings: prompt and context engineering, long-context document processing, structured and constrained output generation, and retrieval systems (embeddings, hybrid search, re-ranking).
  • Working knowledge of applied statistics for evaluation.
  • 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:
  • Specialization in predictive health or medical applications is considered an asset.
  • Familiarity with and hands-on experience with unstructured text data.
  • Experience serving open-weight LLMs (e.g. vLLM), parameter-efficient fine-tuning (LoRA/QLoRA), and quantization for cost- constrained deployment.
  • Publication record in peer-reviewed academic conferences or relevant journals in machine learning.
  • Experience/familiarity with software engineering best practices.
  • Experience with deploying machine learning models in production environments or strong software engineering (or MLE) skills is a plus.
Non-Technical Requirements:
  • Desire to take ownership of a problem and demonstrate 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.
  • Comfortable working in an agile environment and able to pivot as required by project demands
  • 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
  • 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.

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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