AI Platform Engineer

Superhuman

About The Position

We exist so that professionals end each day feeling happier, more productive, and closer to achieving their potential. Our customers get through their inboxes twice as fast; many see inbox zero for the first time in years. Today we are… The fastest email experience in the world Loved and adored: see what our customers say 📣 We've joined forces with Grammarly to build the AI-native productivity suite of the future, with Superhuman as the central communication layer. This partnership accelerates our mission to help professionals achieve their potential — now at even greater scale. Come shape the future of email, communication, and productivity! At Superhuman, we deeply understand how to build products that people love. We incorporate fun and play; we infuse magic and joy; we make experiences that amaze and delight. It all starts with the right team — a team that deeply cares about values, customers, and each other. We're not solving a small problem, and we're not addressing a small market. We're going after email; the one activity that consumes more of our work day than any other. Our ambition doesn't stop there. Next: calendars, notes, contacts, and team communication. We are building the productivity platform of the future. We have created the frameworks for how to build product market fit and redefined the narrative of how to onboard customers successfully. We have shown the world it’s possible to build a premium productivity brand. Our investors included Andreessen Horowitz, First Round Capital, IVP, Tiger Global Management, Sam Altman, and the founders of Gmail, Dropbox, Reddit, Discord, Stripe, GitHub, AngelList, and Intercom. This time, we’re swinging beyond the fences and fundamentally rethinking how individuals and teams should collaborate. We are building a household brand and a worldwide organization. We are here to do the best work of our lives, and we hope you are too.

Requirements

Experience: You have 4+ years of software development experience with a focus on backend engineering, DevOps, ML Ops, or SRE work. You're proficient in at least one back-end programming language (ideally Go). You have hands-on experience with observability, metrics, and monitoring systems.
AI Enthusiast: You believe AI will revolutionize how we work as well as the experiences that we create for our customers. Driven by passion and curiosity, you leverage AI to dramatically increase your own productivity and the impact of your team.
Metrics-Driven: You understand percentiles (P90, P95), know how to build meaningful dashboards, and can turn raw data into actionable insights. You've worked with monitoring and observability tools.
Systems Thinker: You think in terms of pipelines, lifecycles, and closed loops. You know how to build scalable infrastructure that enables other teams to move faster.
Remarkable Quality: You produce work that is striking, worthy of attention, and a contribution to the state of the art.
Asynchronous Communicator: You’re effective across various mediums (especially Slack, Notion, and email) and can produce and consume detailed written materials as needed without sacrificing speed. You respond quickly and thoughtfully to unblock others and speed things up.
Start-to-Finish Ownership: Acts with 100% responsibility for their own outcomes as well as the outcomes of the company.
Bias to action: Speed matters. Takes rapid and decisive steps forward, even in the face of uncertainty, recognizing action is the catalyst for progress and growth.
Growth Mindset: You embrace challenges, welcome feedback, and believe you and others can always grow.

Responsibilities

Own the observability and lifecycle management of AI features across the organization
Build tools and infrastructure to enable teams to develop, monitor, and optimize LLM-powered features
Design and implement closed-loop evaluation pipelines that automatically validate prompt changes
Develop comprehensive metrics and dashboards to track LLM usage: cost per feature, token patterns, and latency.
Create systems that tie user feedback to specific prompts and LLM calls
Establish best practices and processes for the full lifecycle of prompts: development, testing, deployment, and monitoring
Collaborate with engineering teams across the org to ensure they have the tools and visibility needed to build high-quality AI features