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Handshake is hiring a Machine Learning Engineer I for the Growth Relevance team. The company powers 25 million job seekers, 1 million+ employers, and 1,600 educational institutions. In 2025, Handshake launched Handshake AI, a rapidly growing AI data business that has scaled to ~$1B run rate and pays ~$60M monthly to over 30K individuals.
In this role, you will develop, deploy, and enhance machine learning systems focused on lifecycle optimization, personalized notifications, and monetization strategies. You'll work with cutting-edge ML infrastructure including embedding-based retrieval, Graph Neural Networks, and multi-stage rankers built on 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.
Key responsibilities include:
- Developing and iterating on machine learning models and features that directly influence user experience across lifecycle, notifications, and monetization, with guidance from senior engineers
- Partnering with senior engineers, data scientists, and product managers to develop and iterate on ML models that improve product features and user experience
- Growing your technical depth by working alongside experienced ML practitioners and learning best practices in model development, experimentation, and production deployment
You'll be part of a team that includes engineers and scientists from Palantir, Meta, Scale AI, and former YC founders, working hand-in-hand with frontier AI lab researchers and Fortune 500 partners.
REQUIREMENTS:
- Bachelor's degree in Computer Science, Data Science, or a related field
- 1-3 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
- Experience with deploying and evaluating agentic workflows
- Strong foundation in core ML concepts, including classification, regression, ranking, and model evaluation
DESIRABLE:
- Master's degree or currently pursuing an advanced degree in a relevant field
- Exposure to recommendations, personalization, NLP, deep learning, LLMs, or explainable AI
- Familiarity with the ML lifecycle (experiment tracking, model monitoring, feature pipelines)
- Experience with cloud platforms (GCP, AWS, or Azure)
- Clear communication skills and ability to translate technical work for diverse audiences
- Collaborative mindset with cross-functional experience with product, analytics, and engineering teams
