Staff Machine Learning Engineer

Handshake•San Francisco, CA

5d•$238,000 - $297,000•Remote

About The Position

Handshake is hiring a Staff 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 providing innovative, responsible AI-powered solutions that guide students from educational aspirations to meaningful career opportunities. In this role, you will set the technical direction for the systems that power core embedding models, consumer job search & recommendations, user understanding, and personalized notifications across the Handshake platform. You'll operate as a force multiplier — architecting the ML infrastructure that the broader Relevance org builds on, and raising the technical bar for how the team ships models into production. You'll own the roadmap for a suite of systems built on multiple retrieval models — Graph Neural Network, bi-encoders, semantic cross-encoders with multi-stage rankers, running on a data platform with billions of data points. In addition, the team is investing into areas of generative retrieval and post-training. Your work will directly move the marketplace's core metrics, and you'll be a key voice in how Handshake approaches explainability, fairness, and quality as we scale responsible AI across the product.

Requirements

8+ years of experience in machine learning, data science, or a related field, with a track record of owning large-scale, production ML systems end-to-end
Deep expertise in Python and ML frameworks such as scikit-learn, PyTorch, or TensorFlow
Experience in recommendations, personalization, NLP, deep learning, LLMs, or explainable AI
Deep familiarity with the ML lifecycle (experiment tracking, model monitoring, feature pipelines) at scale
Demonstrated ability to architect and scale ML infrastructure — embedding-based retrieval, ranking systems, GNNs, or similar — in a high-traffic cloud based production environment
Strong foundation in core ML concepts (classification, regression, ranking, model evaluation) with the judgment to know when and how to apply them
Experience setting technical direction across teams and mentoring senior and mid-level engineers
Track record of driving measurable business impact through ML systems at scale

Nice To Haves

Experience with Generative Retrieval and LLM Post Training recipes is a plus.
A track record as a clear, persuasive communicator who can align technical and non-technical stakeholders around a shared roadmap
Experience building or scaling a team's technical practices and standards from the ground up

Responsibilities

Define the technical strategy and system design for ML models and infrastructure spanning search and recommendation, notifications, generative retrieval, and core embeddings — making build-vs-buy, architecture, and platform decisions with company-wide impact.
Set technical standards and best practices for model development, experimentation, and production deployment; mentor and elevate engineers and data scientists across the team.
Partner with engineering leadership, product, and data science to translate ambiguous business problems into a clear technical roadmap, and drive alignment across stakeholders on priorities and tradeoffs.
Get hands-on where it matters most — building and shipping the highest-leverage models and systems yourself, and unblocking the team on the hardest technical problems.