Handshake
Machine Learning Engineer I, Growth Relevance
About this role
Handshake seeks an ML Engineer to build personalization and matching systems serving millions of job seekers and employers. You'll own machine learning projects end-to-end, from data pipeline to production deployment, leveraging cutting-edge techniques like embeddings and graph neural networks.
What you'll do
- Develop and iterate on ML models for user personalization, discovery, and job matching
- Own the full ML lifecycle including data preparation, model training, experimentation, and deployment
- Collaborate with senior engineers, data scientists, and product managers on feature development
- Contribute to responsible AI practices including explainability and fairness
- Work with large-scale datasets (billions of data points) across retrieval and ranking systems
- Partner cross-functionally with product and analytics teams to drive business metrics
What they're looking for
- Python programming
- Machine learning frameworks (PyTorch, TensorFlow, scikit-learn)
- ML fundamentals (classification, regression, ranking, model evaluation)
- Recommendations and personalization systems
- Deep learning and NLP
- ML lifecycle management (experiment tracking, monitoring, feature pipelines)
- Cloud platforms (GCP, AWS, or Azure)
- Cross-functional communication skills
Benefits
- Equity ownership in fast-growing company
- 401(k) match
- Competitive compensation
- Financial coaching
- Mentorship from experienced ML practitioners
- Work on products impacting millions globally
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Handshake
Handshake is a platform that connects students and job seekers with employers through job discovery and matching. The company is hiring iOS engineers, machine learning engineers, AI software engineers, and AI domain experts to build personalization systems, mobile experiences, and production AI solutions for both consumer and enterprise customers.
- Website
- handshake.com
Likely interview questions
- Walk us through a machine learning project where you built a classification or ranking model. What was your evaluation strategy, and how did you measure success?
- Describe your experience with embedding-based retrieval or recommendation systems. How would you approach building a personalized notification system?