Staff Software Engineer

Pebl•Palo Alto, CA

13d

About The Position

Pebl is seeking an exceptional Staff Software Engineer to join their Growth Engineering team. This role is for a technical leader who is also an active practitioner in the AI-augmented development era. The engineer will drive the architecture and delivery of scalable, user-facing systems that improve acquisition, activation, and retention, and will directly shape the AI product experiences at the heart of those initiatives. The role involves building across the full stack using React, TypeScript, Postgres, and AWS, while championing the shift toward “AI-native” engineering by standardizing tools like Claude Code and Codex to maximize team velocity and code quality.

Requirements

Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
8+ years of professional software engineering experience, with strong full-stack depth spanning both front-end (React/TypeScript) and backend/distributed systems.
Hands-on experience across two dimensions — (1) designing and building AI-first, user-facing product features and experiences (e.g., LLM-powered capabilities shipped to real users), and (2) using LLM-powered coding agents such as Claude Code, Codex, and other AI coding assistants to accelerate the SDLC and improve code quality. Demonstrated depth in at least one of these areas and working fluency in the other.
Focus on delivering customer value by minimizing waste, optimizing workflows, fostering continuous improvement, and prioritizing iteratively.
Deep expertise in TypeScript, Postgres, and AWS (Lambda, DynamoDB, API Gateway, S3, etc.), with strong proficiency in React.
Strong understanding of system design, scalability, and performance optimization.
Proven track record of successfully leading technical projects from inception to deployment.
Excellent problem-solving and debugging skills, with a focus on robust, reliable solutions.
Exceptional communication and leadership skills, including the ability to distill complex technical and AI-related trade-offs into actionable plans for leadership and cross-functional partners.

Nice To Haves

Experience in growth-focused engineering projects or A/B testing frameworks.
Familiarity with modern LLM application patterns (e.g., RAG, evaluations, prompt engineering, or agentic frameworks).
Knowledge of front-end performance optimization techniques.
Background in leading or contributing to open-source projects.
Experience with testing frameworks (e.g., Jest, Cypress, or React Testing Library).

Responsibilities

Lead the architecture, design, and implementation of complex, scalable, full-stack systems and services across the Growth domain.
Design and build AI-first, user-facing product experiences and capabilities — turning LLM-powered features into reliable, high-quality product surfaces that drive measurable growth outcomes.
Champion an “AI-first” engineering culture, integrating Claude Code, Codex, and other LLM harnesses into daily workflows to accelerate experimentation, product velocity, and code quality.
Collaborate closely with product managers, designers, and data teams to define the technical roadmap and deliver impactful features aligned with business objectives.
Drive technical decisions and set the bar for coding standards, system design, testing, and process improvements — including how AI tooling is adopted responsibly across the team.
Build, optimize, and maintain applications and services using React JS, TypeScript, Postgres, and AWS (Lambda, DynamoDB, API Gateway, S3) in a serverless environment.
Mentor and guide engineers across the team, balancing human-led mentorship with AI-augmented professional development, and fostering a culture of technical excellence and continuous improvement.
Conduct technical reviews, provide constructive feedback, and ensure high-quality deliverables and rigorous quality benchmarks — even as we accelerate delivery with AI tools.
Act as an SME for the Growth Engineering team, staying ahead of emerging AI and engineering trends and identifying where to incorporate them.
Troubleshoot and resolve high-priority technical issues in production systems, maintaining high observability and stability standards.