Engineering Manager

Sunset•New York, NY

28d

About The Position

At its core, Sunset was founded to help founders. We started by supporting startups through shutting down, but we have since expanded into unlocking a new revenue stream for all types of businesses. In 2025, we had a unique insight: the data every company generates each day through collaboration, communication, and building is some of the most valuable training data in the world. Public and synthetic data can only get frontier models so far, so the next generation of model progress depends on real, proprietary data grounded in how actual businesses operate. We are a primary source of it, partnering directly with the frontier AI labs building what comes next. Why Join Sunset 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.

Requirements

At least two years of experience directly managing engineers who build production software
Hired well, delivered direct feedback, handled performance issues, and developed senior engineers into broader owners
Built or led technically demanding products involving complex workflows, data systems, AI/ML, infrastructure, or sensitive information
Can review code and designs with depth, debug alongside the team, prototype when useful, and step back once ownership is clear
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
Startup experience and enjoy broad ownership, changing context, and incomplete information
Communicate clearly with customers, Product, Design, domain experts, and highly technical ICs

Nice To Haves

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

Responsibilities

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