Applied Machine Learning Specialist

4 days ago

Toronto, Canada Vector Institute Full time

**JOB DESCRIPTION**:
**Applied ML SPECIALIST - Software Development**:
Vector’s AI Engineering team enables researchers and partners in industry, health, and government to accelerate AI deployments that have the potential to unlock transformative benefits. Vector’s growing team of applied machine learning experts provide guidance, expertise, and tailored software tools to support Vector’s partners in collaborative projects through their robust technological and deep learning expertise and extensive compute capacity. AI infrastructure and engineering expertise housed in the AI Engineering team enables world-leading Vector-affiliated researchers to conduct and advance their experiments, pushing the frontiers of AI innovation.

If you are passionate about packaging ML models and research into software artifacts, and thrive in an AI lab environment, we encourage you to apply. Join our team and contribute to enabling ML researchers through robust software engineering practices, paving the way for groundbreaking advancements in artificial intelligence.

**Position Summary**:
We are seeking a highly skilled and innovative Applied ML Specialist to join our AI Engineering team. As an Applied ML Specialist, your primary focus will be on packaging ML models and research into efficient and scalable software artifacts. You will play a pivotal role in enabling ML researchers through robust software engineering practices, facilitating the exploration and implementation of cutting-edge machine learning techniques.

**Key Responsibilities**:

- Collaborate closely with Applied ML scientists and Applied ML Specialists to understand their models, algorithms, and research objectives, and work towards packaging them into reusable software components.
- Develop software libraries, frameworks, and tools that enable ML researchers to effectively experiment, train, evaluate, and deploy their models.
- Implement robust and scalable data pipelines, data preprocessing techniques, and feature engineering methodologies to support ML research and development.
- Collaborate with software engineering teams and ML researchers to optimize and improve the performance, scalability, and reliability of ML software artifacts.
- Stay updated with the latest advancements in machine learning research and software engineering practices, leveraging new tools and techniques to enhance ML research capabilities.
- Contribute to the development of experimentation frameworks and infrastructure, enabling ML researchers to conduct large-scale experiments efficiently.
- Collaborate with stakeholders to understand their requirements and provide software engineering solutions that bridge the gap between ML research and practical implementation.
- Collaborate in mentoring and providing guidance to junior team members, fostering a culture of collaboration and continuous learning within the AI lab.
- Stay informed about emerging trends and best practices in AI and software engineering, sharing knowledge and insights with the team.

**Success Measures**:

- The code quality and extensibility of existing libraries as well as new libraries implemented by AI Engineering is high;
- The software and APIs developed by AI Engineering are well documented and there is strong user engagement, measured in terms of forks, PRs, issues and stars;
- The number of successful products/models delivered by AI Engineering increases, where code is reused and duplicate effort is reduced. The number of APIs scaled to multiple users increases;
- Software development best practices are more widely adopted across the team, showcased by successful design of implementations, reliability, observability and operability; and,
- A high degree of automation of code review and software continuous integration process is achieved. Automation of running of data pipelines, model training and evaluation using workflow orchestration tools is achieved.
- Bachelor's or Master's degree in Computer Science, Data Science, or a related field.
- 2-3 years of relevant experience in software development, with a strong focus on packaging ML models and research findings.
- Solid programming skills in languages such as Python, Java, 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.
- Experience in developing and maintaining software libraries, APIs, and frameworks for ML model integration.
- Proficiency in software engineering best practices, version control systems, and collaborative development environments.
- Familiarity with cloud platforms (e.g., AWS, Azure, or Google Cloud) and containerization technologies (e.g., Docker, Kubernetes) is a plus.
- 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 eff

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