Engineering Manager

About Replay We are building the first-ever bank for enterprise data. There are over 35 million businesses in the US alone, many of which are sitting on an asset they don’t know they have. At Replay, we help businesses monetize their historical operating data by licensing it to the frontier AI and data labs for model training. Replay sits at the intersection of advancing the frontier of AI, while helping companies generate millions in growth capital without having to give up equity, take on debt, or lay off employees. Why Join Replay Now We have scaled from $0 to a multi-eight-figure run rate in a matter of months We have raised from top-tier investors, including Floodgate, Afore, Ludlow, and Hustle Fund We are small enough that you will carry outsized responsibility and grow as quickly as the company does You will partner with and build for some of the fastest and most important companies in the world You will help build a massive, category-defining business from the ground floor The Role The engineering work spans some of the messiest parts of enterprise data: getting internal work data out of the systems where it lives, helping companies navigate dissolution, and de-identifying sensitive datasets without destroying what makes them useful. Doing this well requires thoughtful product interfaces, long-running workflows, data and ML systems, evaluation, permissions, and reliable operations to work as one system. You will build a high-agency engineering team, develop strong independent owners, and turn difficult customer and technical problems into trustworthy products and systems. This is an opportunity to shape the team and its technical operating system, not inherit layers of established process. You will influence who we hire, how ownership is divided, which capabilities become durable infrastructure, how we evaluate quality, and how AI changes the way the team builds. This is a bounded player-coach role. Management is the primary job, but you will stay technically active through design and code review, debugging, incidents, prototypes, and occasional implementation where it creates leverage. You will not carry a standing feature load or become the owner of roadmap-critical implementation. What You'll Do Hire, onboard, coach, and develop engineers into strong independent owners Define ownership and technical direction across the product and systems your team is responsible for Turn customer behavior, product usage, data quality, system health, and team evidence into a sequenced roadmap Review important designs and pull requests, debug difficult failures alongside the team, and lead technical decisions and incidents when needed Prototype or contribute code selectively when it resolves ambiguity, unblocks the team, or creates reusable leverage Establish lightweight practices for planning, evaluation, releases, incidents, quality, and learning Delegate meaningful decisions without becoming detached from the technical work Lead difficult production and customer situations without creating a hero culture Make security, privacy, permissions, AI behavior, and recovery part of product and engineering design Use AI engineering tools directly and establish team practices for speed, review, evaluation, and verification What Success Looks Like Engineers understand what they own and make strong product and technical decisions without waiting for you New hires ramp quickly, senior ICs grow, and new leaders emerge Get Data journeys become more complete, observable, and recoverable, with fewer recurring escalations and less manual rescue Clean Data deliveries become easier to evaluate and investigate, with fewer escaped defects, avoidable reruns, and ambiguous failures The team can distinguish product, source-data, model, evaluation, orchestration, and infrastructure failures—and fix their recurring causes AI improves engineering and product capability without weakening quality, security, or human judgment Delivery becomes more predictable while customer trust, system reliability, and the team's capacity increase Your technical involvement improves the team's judgment and independence rather than making delivery depend on you You Might Thrive Here If You have at least two years of experience directly managing engineers who build production software You have hired well, delivered direct feedback, handled performance issues, and developed senior engineers into broader owners You have built or led technically demanding products involving complex workflows, data systems, AI/ML, infrastructure, or sensitive information You can review code and designs with depth, debug alongside the team, prototype when useful, and step back once ownership is clear You use AI engineering tools fluently and have a practical point of view about where they create leverage, where they fail, and how their work should be verified You have startup experience and enjoy broad ownership, changing context, and incomplete information You communicate clearly with customers, Product, Design, domain experts, and highly technical ICs This Role May Not Be for You If You want a people-management role that stays removed from product and technical decisions You want your ongoing contribution to be a standing feature workload or critical-path implementation You prefer adding process before understanding the product, system, and ownership problem directly You need clean boundaries between product, engineering, data, AI, and customer work You are most comfortable being the permanent decision-maker rather than developing ownership in others You do not want AI tools to be part of your own or your team's daily engineering workflow Bonus Experience leading teams that span software, data, ML, evaluation, or infrastructure Experience with enterprise data connections, workflow-heavy products, or internal tools Experience with de-identification, privacy-sensitive data, compliance, or otherwise high-trust products Experience establishing evaluation, incident, release, or quality systems that improved outcomes without adding heavy ceremony Experience growing an engineering organization through an early-stage or high-change period