Staff Machine Learning Engineer
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 hand-in-hand with world-class AI labs, Fortune 500 partners, and the world's top educational institutions
Work alongside engineers, scientists, operators, and more from Palantir, Meta, Scale AI, and former YC founders
Build a massive, fast-growing business with billions in revenue
About Handshake AI
Human data is the core infrastructure to AI advancement. Frontier AI labs currently improve model capabilities with various data-intensive post-training techniques. We believe that data spend for AI training will increase by 3–5x in the next few years and continue for much longer as models take on new domains. Handshake AI supports all of the frontier AI labs, working on their most complex data at the largest scale.
About the Role
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.
What You'll Do
Architect: 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.
Force Multiplier: Set technical standards and best practices for model development, experimentation, and production deployment; mentor and elevate engineers and data scientists across the team.
Cross-functional Leader: 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.
Operator: 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.
Desired Capabilities
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
Experience with Generative Retrieval and LLM Post Training recipes is a plus.
Extra Credit
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
Perks
Handshake delivers benefits that help you feel supported — and thrive at work and in life.
The below benefits are for full-time US employees.
🎯 Ownership: Equity in a fast-growing company
💰 Financial Wellness: 401(k) match, competitive compensation, financial coaching
🍼 Family Support: Paid parental leave, fertility benefits, parental coaching
💝 Wellbeing: Medical, dental, and vision, mental health support, $500 wellness stipend
📚 Growth: $2,000 learning stipend, ongoing development
💻 Remote & Office: Internet, commuting, and free lunch/gym in our SF office
🏝 Time Off: Flexible PTO, 15 holidays + 2 flex days
🤝 Connection: Team outings & referral bonuses
Explore our mission, values, and comprehensive US benefits at joinhandshake.com/careers.