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Handshake is hiring a Machine Learning Engineer for the Network and Handshake AI Marketplace Relevance team. The role focuses on building and improving machine learning systems that power job search, recommendations, personalized notifications, and core embedding models across the Handshake platform.
You will work with experienced ML engineers, data scientists, product managers, and software engineers to develop models, run experiments, and deploy reliable ML solutions to production. The team works with retrieval and ranking approaches including graph-based models, bi-encoders, semantic cross-encoders, and multi-stage rankers, supported by a data platform containing billions of data points. The team is also exploring emerging areas such as generative retrieval and post-training.
Key responsibilities include building and improving ML models for search, recommendations, notifications, and embeddings; developing, testing, and deploying models in production; working with large datasets to create features and evaluate performance; contributing to retrieval, ranking, and experimentation systems; monitoring production models for quality, reliability, latency, and scalability; partnering with product and engineering teams to translate business needs into ML solutions; participating in technical design discussions and code reviews; and using experimentation and marketplace metrics to measure impact.
Required qualifications: 3+ years of professional experience in machine learning, data science, or software engineering; proficiency in Python and ML frameworks (scikit-learn, PyTorch, TensorFlow); experience building, evaluating, and deploying ML models in production; familiarity with recommendations, search, personalization, ranking, NLP, or deep learning; understanding of core ML concepts; experience with data pipelines and model monitoring; strong software engineering fundamentals; collaborative experience with cross-functional teams; ability to break down complex problems and deliver solutions with support; and focus on measurable results.
Extra credit includes experience with embedding-based retrieval, multi-stage ranking, graph-based models, recommender systems, large-scale datasets, generative retrieval, LLM evaluation, explainable AI, or fairness in machine learning.
