Engineering Lead, AI Email Applications, Artificial Intelligence (AI) Required - Work From Home

Next Step Systems•San Francisco, CA

4d•$170,000 - $190,000•Remote

About The Position

As the Engineering Lead, AI Email Applications, you own the engineering layer that turns Artificial Intelligence (AI) capability into a reliable product. You will shape the architecture across backend services, APIs, and client applications, while leading a small team of senior engineers. This is a hands-on leadership role. You stay close to the code, make critical architectural decisions, and ensure the system remains fast, reliable, and coherent as it scales. This position is 100% Remote. MUST BE WILLING TO TAKE A 60 MINUTE CODING ASSESSMENT.

Requirements

Artificial Intelligence (AI) experience is required.
Significant experience leading application or product engineering for complex systems.
Strong hands-on background building and shipping backend systems used in production.
Experience building AI-powered products or systems that integrate with modern Machine Learning (ML)/Large Language Model (LLM) capabilities.
Strong technical judgment and the ability to make decisions with incomplete information.
Comfortable working across backend and client platforms, with deep expertise in at least one.
A strong bias toward ownership, shipping, and learning from real-world usage.
High standards for code quality, reliability, and engineering fundamentals.

Responsibilities

Own the application engineering strategy and execution across backend, mobile, and desktop.
Design reliable systems for AI-powered workflows, including state, orchestration, tool use, retries, fallbacks, and failure handling.
Build the APIs and abstractions that connect Artificial Intelligence (AI) systems to real-world product experiences.
Lead a small, senior engineering team and raise the technical bar through design reviews, code reviews, and hands-on mentorship.
Make high-impact trade-offs across latency, reliability, cost, security, and user experience.
Establish strong production foundations: observability, monitoring, testing, and operational excellence.
Partner closely with Machine Learning (ML) leadership to turn model capabilities into robust, shippable product experiences.
Move quickly while keeping the architecture simple, maintainable, and ready to scale.