Senior/Staff Full Stack Engineer

 

About Ethos

Ethos is on a mission to bridge the human readiness gap by transforming how training is developed, consumed, and aligned with strategic business outcomes. As a well-funded Series A startup ($40M+ raised), we’re a trusted partner to 150+ enterprise customers across the U.S. military, life sciences, manufacturing, supply chain, and professional sports.

We’re expanding our engineering team to deliver a best-in-class learning platform—smarter, faster, and more optimized. We’ve gone all-in on AI tooling in our development process, and we’re accepting and expanding upon the best new practices for creating software in this era.

 

About the Role 

This is a backend-leaning full stack role. You'll work across our Go backend services and React/TypeScript frontend, to continue modernizing our platform in an AI-friendly architecture. We're looking for engineers who treat AI tools as integral to their workflow, and as force multipliers that fundamentally change how software gets built.

As a Senior engineer, you'll own end-to-end feature delivery across the stack and shape architecture. As a Staff engineer, you'll also be a key leader on our team, guiding others and driving technical strategy.

If you love hard systems problems, can operate confidently across backend and frontend, and embrace AI-augmented development as the way forward, this role is for you.

What You’ll Do

• Full-Stack Feature Delivery: Build end-to-end features spanning Go services and React/TypeScript UI; own features from requirements through deployment and post-release iteration.
• AI-Augmented Development: Use AI coding tools (Claude Code, Cursor, Copilot, etc.) as daily workflow tools; build and improve agentic workflows and AI-powered product features.
• Performance & Reliability: Improve latency/throughput; apply caching, connection pooling, rate limiting, backpressure, idempotency, and resilient retries.
• Quality by Default: Raise the bar on testing (unit/integration/e2e), code quality, observability hooks, and safe rollout/rollback patterns.
• Technical Leadership: Lead design reviews, mentor teammates, and drive consistent standards.

Measures of Success (First 6–12 Months)

• AI Workflow Adoption: Demonstrate measurable productivity gains through personal use of AI tooling; contribute to team AI practices and agentic workflow patterns.
• Delivery: Increase feature throughput and deployment frequency without raising change failure rate.

30/60/90 Day Plan

First 30 Days — Map & Baseline

• Land first improvements (bug fixes, hotspots, small perf wins).
• Establish and prove the effectiveness of your personal workflow with AI tooling.

60 Days — Design & Deliver

• Deliver an entire feature end-to-end that includes frontend improvements integrated with backend services.
• Introduce or improve resilience patterns: timeouts, circuit breakers, idempotency, DLQs and data-migrations.

90 Days — Scale & Standardize

• Drive sustained performance gains (caching, query optimization, parallelism) and lower cost per request.
• (Staff) Contribute to the 12–18 month technical roadmap (service map, shared libs, data/event strategy, AI integration patterns).

Basic Qualifications 

• 5+ years building enterprise-scale software systems with full-stack experience.
• Active, fluent use of AI dev