Machine Learning Resident - Client: ConeTec (1 year term)

“Join us in tackling one of the oil & gas industry’s most critical safety challenges: monitoring of tailing ponds. We are looking for an ML researcher or engineer to build efficient, ML-based systems for identifying and flagging site composition risks. You’ll collaborate with domain experts, engineers and scientists to collect and curate a novel site characterization dataset, utilizing tools spanning LLMs, OCR, and other extraction techniques, to then explore Deep Learning and Transfer Learning approaches to build predictive ML systems.”

 - Dave Staszak, Lead Machine Learning Scientist, Advanced Technology

Description

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, ConeTec, 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

ConeTec is a leading provider of geocharacterization services worldwide, specializing in subsurface investigation to help clients explore, build, and protect the world with confidence. By reducing geological uncertainty, ConeTec supports better decision‑making through the delivery of high‑quality, reliable geotechnical information.

ConeTec employs skilled professionals and leverages innovative tools, technologies, and methods to safely acquire high‑quality data for geotechnical, geoenvironmental, and mining applications. ConeTec is committed to fostering an excellent work environment with strong opportunities for professional development, while maintaining the safety of all personnel as its highest priority.

About the Project

This project focuses on developing modern machine-learning solutions to improve geotechnical site characterization. A core component involves building AI-assisted tools to automatically process large volumes of publicly available reports and convert them into structured, high-quality datasets by extracting key engineering parameters and standardizing data formats for integration with ConeTec’s geospatial database. In addition, the project aims to advance ML-driven Cone Penetration Testing (CPT)-based approaches for improving site characterization. By leveraging historical datasets and ConeTec’s extensive in-situ measurements, the work will support the development of predictive models that enhance subsurface understanding.

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, specifically with unstructured data extraction methods (OCR-, LLM-based, etc.) experience and a solid understanding of predictive ML techniques (including Deep Learning, classification/regression, etc.).

Key Responsibilities:

• Design, implement, optimize, and evaluate models to accurately extract and collate scientific data from public and private sources.


• Prepare, curate, and preprocess unstructured data into high-quality datasets to then use for predictive model training, fine-tuning, and validating.


• Utilize state-of-the-art LLM and ML frameworks, tools and open-source libraries to enhance model performance, accelerate workflows, and optimize data processing.  


• Conduct applied research on LLM and ML techniques, with a focus to understanding and addressing the limitations of existing models


• Optimize ML pipelines to ensure efficiency, scalability, and real-time processing capabilities.


• 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 scientific/engineering graduate degree program) MSc. or PhD.


• Proficient in developing and training, fine-tuning and evaluating machine learning and deep neural network models in PyTorch and/or TensorFlow.


• Proficient in Python programming language and related LLM and ML frameworks, libraries, and toolkits (e.g., Scikit-learn, PyTorch, Pandas, HuggingFace, LangGraph).


• Solid understanding of classical statistics and its application in model validation.


• 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:

• Familiarity with and hands-on experience with both unstructured and structured data.


• Publication record in peer-reviewed academic conferences or relevant journals in machine learning (specifically LLM work or applied ML applications)


• 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 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 date of March 4, 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 and the role. 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.