Senior Fullstack Product Engineer

Single Grain•Los Angeles, CA

7d•Remote

About The Position

Single Grain is seeking a Senior Full Stack Product Engineer who possesses a founder's mindset and a product owner's strategic thinking. This role is pivotal as the company transitions into a tech-enabled services (TES) model, where software, automation, and services converge to create leverage. The engineer will be responsible for identifying high-impact product opportunities, overseeing their end-to-end development, measuring their revenue influence, and iterating based on data. A key expectation is the utilization of AI to enhance productivity, speed, and quality. The primary performance indicator for this role is to increase the Free Trial to Paid Conversion rate from 20% to 40%. This is a role that demands full ownership from ideation through to measurement and optimization, rather than simply executing predefined tasks.

Requirements

3+ years of full stack engineering experience, preferably at an early-stage startup, with demonstrated growth in scope or ownership.
Proven experience building and shipping a product end-to-end that users paid for or actively used, with the ability to discuss personal contributions, challenges, and iterations.
A product-centric approach, framing engineering decisions around conversion, retention, and expansion metrics.
Advanced AI proficiency: Demonstrated ability to build repeatable AI workflows, quantify time savings, and actively seek new AI applications.
Comfort with ambiguity and full ownership: Ability to analyze data, form hypotheses, build tests, ship solutions, and measure results independently.
Full stack capability: Must be comfortable working across the entire stack, not limited to pure backend or frontend.

Nice To Haves

Experience in Product-Led Growth (PLG) environments.
Experience with A/B testing frameworks.
Experience with growth experiment design.
Experience with event instrumentation tools.

Responsibilities

Own the conversion funnel: Audit the free trial to paid activation flow, identify drop-off points using data, and implement improvements to increase conversion rates.
Independently ship production code: Build and deploy backend, frontend, and infrastructure changes without reliance on a dedicated DevOps team.
Run structured growth experiments: Launch at least two activation experiments per month by the sixth month, with proper event analytics instrumentation to measure their impact.
Build in-product expansion triggers: Design and implement a system to identify and surface expansion opportunities within the product to users.
Operate AI-native workflows: Utilize AI tools (e.g., Claude, Cursor) for scaffolding, refactoring, and test creation. Document repeatable prompt workflows and contribute to internal AI Standard Operating Procedures (SOPs), aiming to reduce development cycle time by over 40% within 12 months.
Submit a revenue-focused roadmap within 30 days: Audit onboarding and activation processes, identify the top three revenue-impacting friction points, and present a 12-month product roadmap supported by data.