Ethos is a London-based expert network startup that uses voice-powered onboarding and AI to match domain experts with companies seeking specialized knowledge.
<p> </p> <p><span style="font-size: 14pt;">About Ethos </span></p> <p><span style="font-size: 10pt;">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.</span></p> <p><span style="font-size: 10pt;">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.</span></p> <p> </p> <p><span style="font-size: 14pt;">About the Role </span></p> <p>This is a backend-leaning full stack role. You'll work across our <strong>Go backend services and React/TypeScript frontend</strong>, 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.<br><br>As a <strong>Senior</strong> engineer, you'll own end-to-end feature delivery across the stack and shape architecture. As a <strong>Staff</strong> engineer, you'll also be a key leader on our team, guiding others and driving technical strategy.<br><br><em>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.</em></p> <p><span style="font-size: 14pt;">What You’ll Do </span></p> <ul> <li><strong>Full-Stack Feature Delivery:</strong> Build end-to-end features spanning Go services and React/TypeScript UI; own features from requirements through deployment and post-release iteration.</li> <li><strong>AI-Augmented Development:</strong> Use AI coding tools (Claude Code, Cursor, Copilot, etc.) as daily workflow tools; build and improve agentic workflows and AI-powered product features.</li> <li><strong>Performance & Reliability:</strong> Improve latency/throughput; apply caching, connection pooling, rate limiting, backpressure, idempotency, and resilient retries.</li> <li><strong>Quality by Default:</strong> Raise the bar on testing (unit/integration/e2e), code quality, observability hooks, and safe rollout/rollback patterns.</li> <li><strong>Technical Leadership:</strong> Lead design reviews, mentor teammates, and drive consistent standards.</li> </ul> <p><span style="font-size: 14pt;">Measures of Success (First 6–12 Months)</span></p> <ul> <li><strong>AI Workflow Adoption:</strong> Demonstrate measurable productivity gains through personal use of AI tooling; contribute to team AI practices and agentic workflow patterns.</li> <li><strong>Delivery:</strong> Increase feature throughput and deployment frequency without raising change failure rate.</li> </ul> <p><span style="font-size: 14pt;">30/60/90 Day Plan</span></p> <p><strong>First 30 Days — Map & Baseline</strong></p> <ul> <li>Land first improvements (bug fixes, hotspots, small perf wins).</li> <li>Establish and prove the effectiveness of your personal workflow with AI tooling.</li> </ul> <p><strong>60 Days — Design & Deliver</strong></p> <ul> <li>Deliver an entire feature end-to-end that includes frontend improvements integrated with backend services.</li> <li>Introduce or improve resilience patterns: timeouts, circuit breakers, idempotency, DLQs and data-migrations.</li> </ul> <p><strong>90 Days — Scale & Standardize</strong></p> <ul> <li>Drive sustained performance gains (caching, query optimization, parallelism) and lower cost per request.</li> <li><strong>(Staff)</strong> Contribute to the 12–18 month technical roadmap (service map, shared libs, data/event strategy, AI integration patterns).</li> </ul> <p><span style="font-size: 14pt;">Basic Qualifications</span> </p> <ul> <li>5+ years building enterprise-scale software systems with full-stack experience.</li> <li>Active, fluent use of AI development tools as part of your daily workflow.</li> <li>Strong Go skills (or deep experience with another typed backend language, and ability to ramp quickly in Go).</li> <li>Proficiency with React/TypeScript or equivalent modern frontend framework.</li> <li>Strong API design skills (REST/gRPC).</li> <li>Product-mindful: translate user stories into technical plans and make pragmatic trade-offs.</li> <li>Demonstrated excellence in system design, testing strategy, CI/CD, and code quality.</li> <li>Clear communication and collaboration across engineering and product.</li> <li>Comfortable in a startup environment—ownership, autonomy, and bias for delivery.</li> </ul> <p><span style="font-size: 14pt;">Preferred Qualifications</span></p> <ul> <li>Experience building or contributing to agentic workflows, AI-powered features, or LLM integrations (RAG, agents, model-driven pipelines) with attention to cost, latency, and safety/guardrails.</li> <li>Comfort working across the full stack—you don't draw hard lines between "frontend" and "backend."</li> <li>Deployed Go services with <strong>Docker</strong> and <strong>Kubernetes</strong> (Helm; Argo CD/Flux); blue-green/canary rollouts.</li> <li>Strong observability chops (OpenTelemetry, Prometheus/Grafana, Datadog) and performance profiling at scale.</li> </ul> <p> </p> <p><span style="font-size: 14pt;">Tooling you might touch</span></p> <p>We use technologies similar to and including some of these to build our products: </p> <ol> <li>AI development tools (Claude Code, Cursor, GitHub Copilot), Go, gRPC/REST; PostgreSQL; Redis; RabbitMQ; Grafana; GitHub Actions; Docker, Kubernetes; OAuth/OIDC; S3/object storage; React/TypeScript/Expo</li> </ol> <p> </p> <p><span style="font-size: 14pt;">Compensation & Benefits</span></p> <ul> <li style="font-size: 10