Associate Software Developer-Machine Learning

Vector Institute•Toronto, ON

4d•CA$80,500 - CA$100,600•Hybrid

About The Position

As an Associate Software Developer, Machine Learning, your primary focus will be enabling the development of open-source tools, including a flagship product that results in strong outcomes for all stakeholders. You will play a key role in supporting ML researchers through strong software developer practices, helping them explore and implement cutting-edge machine learning techniques.

Requirements

Bachelor's or Master's degree in computer science, data science, or a related field;
Solid programming skills in languages such as Python, JavaScript, or C++, and proficiency in ML frameworks like TensorFlow, PyTorch, or scikit-learn;
Strong understanding of machine learning algorithms, statistical modeling, and data preprocessing techniques;
Strong fundamental understanding of data structures, algorithms, and time complexity;
Proficiency in software engineering best practices, version control systems, and collaborative development environments;
Strong problem-solving abilities, with the capability to bridge the gap between ML research and practical software engineering solutions;
Excellent communication and collaboration skills, with the ability to work effectively in a research-driven and dynamic environment.

Nice To Haves

Familiarity with modern toolkits – LangChain, agent development kits is considered an asset;
Familiarity with cloud platforms (e.g., AWS, Azure, Google Cloud) and containerization technologies (e.g., Docker, Kubernetes) is considered an asset;

Responsibilities

Collaborate with Applied ML Scientists and Applied ML Specialists to understand models, algorithms, and research goals, and package them into reusable software components;
Contribute to the development of Vector AI Engineering’s flagship product using best practices for collaborative software development;
Build libraries, frameworks, and tools that enable efficient experimentation, training, evaluation, and deployment of ML models;
Develop scalable data pipelines, preprocessing workflows, and feature engineering systems to support ML research;
Design and implement APIs, interfaces, and documentation to integrate ML models into production applications;
Collaborate with software development staff and ML researchers to optimize and improve the performance, scalability, and reliability of ML software artifacts;
Apply modern machine learning and software development practices to enhance research productivity and system capabilities;
Support the development of internal agents and maintain critical production services;
Partner with stakeholders to translate research needs into practical, production-ready solutions;
Share knowledge of emerging tools, techniques, and best practices across the team; and,
Other related duties as assigned from time to time.