Machine Learning Engineer
Toronto
Arteria AI
About Arteria AI
Arteria AI is made up of a passionate, rapidly-growing team of lawyers, data scientists, developers and designers, with a vision to build transformative, AI-enabled solutions for the business world. Our mission is to help companies transform how they interact with their most important documents—contracts.
With our award-winning technology in intelligent contracting, we’re looking for enthusiastic individuals who want to grow and win with us!
Responsibilities
As an ML engineer at Arteria AI, you’ll play a key role in developing products that are powered by machine learning as part of our enterprise SaaS platform for digital contracting.
This is a great role for anybody interested in making an impact and joining a friendly team that’s driven by innovation and growth. Our ideal candidate is a curious ML engineer, with strong problem-solving skills, and a deep understanding of how to productionize ML models. We are working as a team, and we are looking for great team players who enjoy solving challenging problems.
Qualifications
- Knowledge of software development practices, version control, and collaboration tools (e.g., Git, GitLab, GitHub).
- Proven ability in Python programming and ML frameworks (PyTorch and Hugging Face)
- Understanding of containerization technologies (e.g., Docker) and orchestration tools (e.g., Kubernetes).
- Experience in product level coding, and software principles
- Demonstrated experience in ML model serving by creating APIs (KServe, Ray Serve, Django, FastAPI, etc.) and experience in engineering, integrating, evaluating, deploying and operationalizing ML/DL models)
- Passion for challenges, learning new things, and solving problems in a clean and modular way in a fast paced startup environment.
* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: APIs Django Docker Engineering FastAPI Git GitHub GitLab KServe Kubernetes Machine Learning ML models Python PyTorch
Perks/benefits: Career development Startup environment
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