Staff Software Engineer, Onboarding

🏗️ Foster and Guide the Technical Strategy for Onboarding

Define and accelerate the backend architecture for a newly onboarded user: the signal-collection systems that learn who a new Redditor is, the personalization pipelines that turn those signals into a tailored first feed, and the experimentation infrastructure that lets us iterate on the moments that decide whether someone becomes a lifelong user. You won't be following a roadmap; you'll shape how we build the systems that compound for every user's lifetime.

🤝Partner With the EM to Cultivate a Growth Engineering Mindset

Growth teams operate differently than platform teams. We prioritize experimentation and iteration, which means sometimes we throw things away to move faster. You'll help the team internalize that operating model: what it means to build for learning, how to balance velocity with reliability, and how to stay rigorous when moving fast.

🤖Scale Impact Through Collaborative Engineering

You'll build and maintain deep partnerships with the engineering teams whose surfaces directly shape a newly onboarded user's experience: Relevance, Feeds, Notifications, and beyond. Many of these collaborations will touch ML-powered systems that surface the right content to the right users. You'll be the bridge that ensures Onboarding has a technical representation for the team and a collaborative influence on their roadmaps.

📊Instrument, Measure, and Iterate

Establish the instrumentation and experimentation foundations that let the team run rigorous A/B tests on growth levers. Use metrics to make the case for the bets worth taking, and deprioritize efforts that don't meet goals.

🔗Guide Cross-Functional Alignment

Serve as the engineering voice in XFN partnerships with Product, Design, Data Science, and other Consumer platform teams. You'll align stakeholders on technical feasibility and tradeoffs, co-author roadmaps that bridge user experience goals with backend realities, and represent Onboarding Engineering in Reddit-wide technical forums.

⚡Raise the Bar on Engineering Excellence

Advocate for reliability, scalability, and maintainability across a complex, high-scale distributed system. Lead design reviews, establish patterns that the broader organization can learn from, and ensure that moving fast never becomes an excuse for accruing crippling tech debt. You'll define what "great" looks like on this team.

Who You Might Be

• At least 7 years of experience. You’ve steered cross-team projects that solved tangible business problems, rather than just hitting a 'ship' date.
• A strategic partner, not just a technologist. A systems thinker. You see how the code, the product, and the team dynamics all influence each other. You can plan for next year while still helping an engineer through a blocker today.
• You're a mentor who helps other engineers level up. You don't just assign tasks; you help the team work through complex problems more effectively. You're energized by the opportunity to build culture and craft on a team, not just software.
• You're deeply data-informed and logical. You ensure metrics are part of the design process, not just an afterthought in a dashboard. Your architectural directions are traceable back to product and business outcomes, and you're fluent in the language of experimentation, funnels, and retention.
• Comfortable with growth-team ambiguity. You navigate ambiguity with confidence when the goal is clear but the path is not. You're comfortable with growth-team speed—prototyping fast, moving on when the data doesn't back a bet, and keeping the team focused when the plan changes.
• A focus on clear, transparent communication. You can effectively distill complex technical trade-offs for any audience—whether you’re collaborating on a whiteboard with peers or presenting a roadmap review to executive stakeholders.
• Experience with ranking, recommendations, and personalization. You have a proven track record of working on ML-heavy products. You can design the backend architectures, retrieval systems, and inference pipelines required to scale them. You understand the nuances of candidate generation and scoring, and you can partner effectively with Machine Learning engineers to turn model outputs (such as Two-Tower models) into high-impact product features.