Design, develop, and deploy robust machine learning and AI systems for a range of products and use cases, including generative AI.
Integrate ML and AI models into production systems, ensuring scalability, reliability, and maintainability.
Deploy and monitor machine learning models and systems, including CI/CD pipelines, automated testing, monitoring, and model versioning.
Leverage cloud platforms (AWS + Snowflake) and ML infrastructure (e.g., SageMaker, feature stores) for scalable deployment.
Collaborate with cross-functional teams (Product, Customer Experience, and other engineering teams) to deliver AI-driven features and insights.
Ensure all ML/AI solutions adhere to best practices in data privacy, security, and ethical standards.
Contribute to a culture of technical excellence, knowledge sharing, and continuous learning.

University degree in Computer Science, Machine Learning, Data Science, or a related field.
3+ years’ experience building and deploying production-grade machine learning models.
Strong software engineering mindset — ability to write clean, reusable, and scalable code in Python.
Experience integrating ML/AI models into production software systems.
Solid understanding of MLOps practices, CI/CD pipelines, and automated testing frameworks.
Hands-on experience with ML frameworks (e.g., TensorFlow, PyTorch, Hugging Face).
Experience working with backend teams and deploying end-to-end products.

Receive a competitive reward package – reevaluated each year – that includes salary, benefits, and pre-IPO equity
Enjoy 28 days of paid vacation, plus an additional day after 2 and 4 years
Make an impact on the environment and society with 1 (fully paid) Impact Day
Receive generous family leave, child support, mental health support, and sabbatical opportunities
We enjoy gathering for meals, cultural initiatives, and events like local Summer Sessions and year-end celebrations; there's also healthy snacks, drinks, and a weekly catered lunch.

Machine Learning Engineer

Key skills