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New York, New York, United States
Hybrid
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.
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 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.
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
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 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
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
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
