“If you are excited about leveraging machine learning, deep learning, and state-of-the-art vision foundation models for computer vision in biotechnology, this is the right opportunity for you. Be a part of a team of research and machine learning scientists and get mentored by some of the best minds in AI while doing it.”
- Atefeh Shahroudnejad, 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 afterwards (note: at the discretion of the client). The resident will be reporting to an Amii Machine Learning Scientist and regularly consult with the Client team to share insights and engage in knowledge transfer activities.
About our Client
Our client is a fast-growing biotech startup based in the San Francisco Bay Area that is developing a platform for multi-modal analysis of cells that heavily leverages imaging for data acquisition. The company is staffed with a world class team of engineers, scientists, team builders and problem solvers that are passionate about developing the next generation technologies that will unravel the complexities of biology.
About the Project
The aim of this project is to develop, train, and evaluate Machine Learning models for object detection and segmentation to be applied to live cell images. You will delve into advanced deep learning techniques, potentially working with vision foundation models like ViTs and SAM, to enhance cell morphology detection, improve the overall efficiency of the platform, and ensure that models are generalizable to diverse cell types.
Required Skills / Expertise
Are you passionate about building great solutions? Do you want to drive a high impact 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, deep learning, and computer vision.
Key Responsibilities:
Build, train, and evaluate ML models for computer vision tasks, especially object detection and segmentation
Prepare data for ML training
Undertake applied research on ML techniques to address the limitations in existing models
Collaborate with the project team and stakeholders to develop MVP and client-focused solutions
Participate in regular meetings with the client, preparing presentations and reports
Required Qualifications:
Completion of a graduate-level program or higher (M.Sc/Ph.D) in Computing Science, Machine Learning, or Engineering with a specialization in Computer Vision
Solid understanding and experience in applying different deep learning models and techniques for computer vision tasks such as image segmentation, object detection, and etc
Experience with advanced models such as Transformers and ViTs
Experience using classical computer vision tools and libraries (e.g., OpenCV, Pillow)
Proficient in developing, training, and fine-tuning deep neural network models in PyTorch and Tensorflow/Keras
Proficient in Python programming language and related ML frameworks, libraries, and toolkits (e.g., Hugging Face Transformers, TIMM, and Scikit-learn)
Familiarity with Ubuntu, SSH, Git, 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:
Expertise in few-shot learning and Fine-tunning large transformer-based models with limited labeled data
Publication record in peer-reviewed academic conferences or relevant journals in machine learning (specifically machine vision)
Experience with deploying machine learning models in production environments or strong software engineering (or MLE) skills is a plus
Familiarity with AWS and GCP
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 May 26, 2025 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.
