Machine Learning Engineer I, Network

Handshake•San Francisco, CA

2d•$151,000 - $189,000•Hybrid

About The Position

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.

Requirements

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 TensorFlow
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 software engineering fundamentals and the ability to write reliable, maintainable code
Experience working collaboratively with engineers, data scientists, product managers, and other cross-functional partners
Ability to break down moderately complex problems, evaluate tradeoffs, and deliver solutions with support from senior team members
A focus on measurable results and improving the end-user experience

Nice To Haves

Experience with embedding-based retrieval, multi-stage ranking, graph-based models, or recommender systems
Experience working with large-scale datasets or high-traffic cloud-based production systems
Familiarity with generative retrieval, LLM evaluation, or post-training techniques
Interest in explainable AI, fairness, or responsible machine learning
Clear communication skills and an interest in contributing to team practices and technical standards

Responsibilities

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