Machine Learning Engineer I

Handshake•San Francisco, CA

1d•$151,000 - $189,000•Remote

About The Position

Machine Learning is at the heart of Handshake's mission to democratize access to opportunity. Every student deserves to be seen, not just by any employer, but by the right one. Machine learning is how we personalize discovery, match intent with opportunity, and drive better outcomes at scale. This role offers end-to-end ownership of the ML lifecycle, from data to deployment, with direct product impact on millions of users. You will work with cutting-edge infrastructure including embedding-based retrieval, Graph Neural Networks, and multi-stage rankers, while contributing to responsible AI practices like explainability, fairness, and quality. The role involves working with billions of data points and collaborating with a world-class team from companies like Scale AI, OpenAI, xAI, Notion, Coinbase, and Palantir.

Requirements

Bachelor’s or advanced degree in Computer Science, Data Science, or a related field
3 years of experience in machine learning, data science, or a related area
Proficient in Python, with hands-on experience in frameworks such as scikit-learn, PyTorch, or TensorFlow
Strong foundation in core ML concepts, including classification, regression, ranking, and model evaluation

Nice To Haves

Exposure to areas such as recommendations, personalization, NLP, deep learning, LLMs, or explainable AI
Familiarity with the ML lifecycle (e.g., experiment tracking, model monitoring, feature pipelines)
Experience with cloud platforms (GCP, AWS, or Azure)
Clear communicator, able to translate technical work for diverse audiences
Collaborative mindset with experience working cross-functionally with product, analytics, and engineering teams

Responsibilities

Develop and iterate on machine learning models and features that directly influence user experience across lifecycle, notifications, and monetization — with guidance from senior engineers.
Partner with senior engineers, data scientists, and product managers to develop and iterate on machine learning models that improve product features and user experience.
Grow your technical depth by working alongside experienced ML practitioners, picking up best practices in model development, experimentation, and production deployment.