Machine Learning Engineer I, Network

About Handshake

Handshake was founded on a simple belief that everyone deserves a path to a great career, regardless of where they went to school or who they know. Today, we power 25 million job seekers, 1 million+ employers, and 1,600 educational institutions.

In 2025, we started Handshake AI and built the fastest-growing AI data business in history. We work directly with frontier AI lab researchers to create evaluations, publish benchmarks, and push the boundary of data. We've grown from $0 to ~$1B run rate and pay ~$60M to over 30K individuals every month.

Why join Handshake now:

• Shape how every career evolves in the AI economy, at global scale, with impact your friends, family, and peers can see and feel
• Partner with world-class AI labs, Fortune 500 companies, and the world’s leading educational institutions
• Work alongside engineers, scientists, operators, and more from Palantir, Meta, Scale AI, and former YC founders
• Help build a massive, fast-growing business with billions in revenue

Human data is the core infrastructure of AI advancement. Frontier AI labs currently improve model capabilities through various data-intensive post-training techniques. We believe spending on AI training data will increase by 3–5x over the next few years and continue growing as models expand into new domains. Handshake AI supports all frontier AI labs, working on their most complex data challenges at significant scale.

Handshake is hiring a Machine Learning Engineer for the Network and Handshake AI Marketplace Relevance team. AI is transforming how students navigate their careers, and we’re committed to developing innovative, responsible AI-powered solutions that connect students with meaningful career opportunities.

In this role, you’ll build and improve the machine learning systems that power job search and recommendations, user understanding, personalized notifications, and core embedding models across the Handshake platform. You’ll work closely with experienced machine learning 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. Your work will directly contribute to improving marketplace outcomes while supporting Handshake’s commitment to explainability, fairness, and responsible AI.

• Build and improve machine learning models for search, recommendations, notifications, user understanding, and embeddings
• Develop, test, and deploy models and supporting services in a production environment
• Work with large datasets to create features, train models, and evaluate performance
• Contribute to retrieval, ranking, personalization, and experimentation systems
• Monitor production models and help improve their quality, reliability, latency, and scalability
• Partner with product, engineering, and data science to translate business and user needs into practical ML solutions
• Participate in technical design discussions, code reviews, and team planning
• Use experimentation and marketplace metrics to measure the impact of your work
• Contribute to team standards and best practices for model development, evaluation, and deployment

• 3+ years of professional experience in machine learning, data science, software engineering, or a related field
• Proficiency in Python and experience with ML frameworks such as scikit-learn, PyTorch, or Tensor Flow
• Experience building, evaluating, and deploying machine learning models in production
• Familiarity with one or more relevant areas, such as recommendations, search, personalization, ranking, NLP, deep learning, or LLMs
• Understanding of core ML concepts, including classification, regression, ranking, feature engineering, and model evaluation
• Experience working with data pipelines, experiment tracking, model monitoring, or other parts of the ML lifecycle
• Strong…