Handshake is hiring a Senior Machine Learning Engineer for the Growth Relevance team. AI is transforming how students navigate their careers, and we're committed to providing innovative, responsible AI-powered solutions that guide students from educational aspirations to meaningful career opportunities. In this role, you will contribute directly to this mission by developing, deploying, and enhancing machine learning systems focused on lifecycle optimization, personalized notifications, and monetization strategies.
You'll join a high-impact team leveraging cutting-edge ML infrastructure, including embedding-based retrieval, Graph Neural Networks, and multi-stage rankers built upon a robust data platform with billions of data points. Your work will drive critical marketplace metrics, enhance user engagement, and contribute to responsible AI practices around explainability, fairness, and quality.
Technical Leader: You'll drive technical solutions, elevate engineering practices, collaborate across teams, and mentor colleagues to deliver impactful ML-driven products.
Innovator: Develop and optimize machine learning models and systems that directly influence user experience across lifecycle, notifications, and monetization.
Collaborator: Partner closely with product managers, data scientists, and engineers to design, execute, and iterate on ML-driven user experiences and experiments, providing key insights to inform strategic product decisions.
You are a hands-on ML engineer with a proven track record in identifying complex problems, crafting thoughtful technical solutions, and executing projects from conception to deployment. You balance technical expertise with strong collaboration skills, possess a relentless focus on user outcomes, and thrive in ambiguity, making informed, data-driven decisions.
3+ years of industry experience building and deploying production ML systems.
Hands-on experience developing, deploying, and optimizing web-scale ML systems.
Experience with ML lifecycle management, personalization systems, notification optimization, and/or monetization-driven algorithms.
Background in one or more ML areas: recommendations, personalization, NLP, deep learning, LLM integration, or explainable AI.
Familiarity building scalable ML applications using cloud infrastructure (GCP, AWS, Azure).