Your impact At Handshake, we believe in the power of community to unlock opportunity for every student and recent grad. As a Senior Staff Software Engineer on our Content & Community team, you’ll architect and scale the backbone of our professional graph—powering how users connect, discover, and engage. Your work will directly shape the way millions of students and employers build networks, discover people and content, and find the right opportunities. You’ll drive the evolution of our people search, profile, and follow graph, and use cutting-edge AI to deliver personalized people recommendations and content feeds that make every connection meaningful.

Your role Build the Social Graph: Design and implement scalable systems for our follow graph, enabling rich connections and powering people recommendations across Handshake.

Deliver Personalized Experiences: Integrate LLM/AI and machine learning into our profile, people search, and feed experiences—ensuring every user receives relevant recommendations and content.

Power the Feed: Own and evolve the backend for our social and content feeds, working cross-functionally with Staff Engineers and product teams to ensure the feed both reflects and shapes the user’s network and interests.

Cross-Cutting Influence: Collaborate across teams to understand how user activity (e.g., likes, follows, feed engagement) influences recommendations and feed ranking, and vice versa—closing the loop between network growth and content discovery.

Backend Leadership: Drive the design, implementation, and scaling of backend services and APIs that support people search, recommendations, and feed personalization at massive scale.

Your experience 8+ years of backend software engineering experience, ideally working on social, community, or graph-based products; full stack development experience is a strong plus.

Proven expertise with distributed systems, data modeling for social graphs, and building scalable APIs.

Experience with machine learning or personalization systems, especially in the context of recommendations or feeds.

Track record of collaborating with cross-functional partners (product, design, ML, and other engineering teams) to deliver impactful user-facing features.

Experience designing and building large-scale search, social graph, or entity tagging (ET) systems.

Bonus areas of expertise Deep experience with people search, profile systems, or social graph infrastructure.

Familiarity with LLM/AI integration into user-facing features.

Experience designing or scaling recommendation engines or ranking systems.

Understanding of how user interactions (likes, follows, shares) influence feed and recommendation algorithms.

Prior experience in social, or high-scale consumer platforms.

Contributions to open-source graph or ML frameworks.

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