Engineering Manager — AI-First Platform & Agent Teams
LumiMeds
Canada, Germany, India, United Kingdom, United StatesremotePosted 16 days ago
Skill Required
Engineering-ManagementAI-EngineeringSoftware-Engineering-ManagementAI-Product-DevelopmentFull-Stack-EngineeringAI-ML-Engineering-ManagerAI-Engineering-LeadLead-AI-Platform-EngineerPlatform-Engineering-ManagerEngineering-ManagerAI EngineerPlatform EngineerEngineering ManagerEngineeringAISOCObservabilityStatisticsdesigninganalyticsbuildingTestNGdesignJiraandFulltime
Key highlights
- Partial US hours overlap required (minimum 4–5 hours daily)
- Seniority: Manager — Player/Coach
- 6+ years of software engineering experience, including 2+ years in a lead or management role
- Claude Agent SDK experience required (non-negotiable)
- Equal opportunity employer
- Direct impact: systems built affect patient outcomes
Role overview
LumiMeds is a fast-growing U.S.-based telehealth startup focused on weight management and long-term metabolic health, building the next generation of e-commerce and clinical infrastructure from the ground up. As an early-stage, remote-first company, they operate with limited bureaucracy and expect high ownership from team members. This is a player/coach role for an engineer-turned-manager who will lead a team of 4–8 full-stack engineers while simultaneously serving as the resident expert in designing and running Claude agent teams—using coordinated AI agents as a structural force multiplier on engineering output.
Responsibilities
- Lead and grow a high-performing engineering team of 4–8 engineers: hire, onboard, coach, and develop; set clear expectations, give direct feedback, and build a culture where velocity and quality are not in tension
- Design and operate Claude agent teams: architect agent pipelines using the Claude Agent SDK to parallelize engineering work at scale—parallel feature development, automated test coverage, documentation generation, code review passes, and spec-to-implementation workflows; define subagent roles, manage inter-agent context handoffs, validate outputs, and escalate to human judgment at the right moments
- Stay in the code: write production code, review PRs with technical depth, debug hard problems, and pair with engineers on work that needs a senior eye
- Set the AI velocity standard: define how the team uses AI tooling—Cursor, Claude Code, agent pipelines—and push the frontier; have strong, specific opinions about how to prompt effectively, which tasks to delegate to agents, and how to verify agent output before it ships
- Own delivery end to end: run sprint planning, resolve blockers, manage dependencies across product, clinical, and infrastructure; own outcomes—not just process
- Write tickets AI agents can execute: create precise, structured, unambiguous specs with acceptance criteria, edge cases, API contracts; ensure Claude Code or a junior engineer can run with them without a sync
- Build and own consumer app and e-commerce systems: ship full-stack consumer products end to end—mobile-backed apps, e-commerce storefronts, subscription billing, checkout flows; understand high-conversion funnel architecture and build or own user behavior tracking infrastructure: event schemas, analytics pipelines, conversion funnels, retention dashboards
- Build and own A/B testing infrastructure: design and maintain the experimentation platform that powers product decisions—feature flags, experiment assignment, statistical significance tracking, and results dashboards; understand holdout groups, novelty effects, and how to run clean experiments across checkout flows, onboarding, and clinical intake
- Build the systems that make the team scale: engineering standards, PR review norms, deployment practices, observability, incident response
- Collaborate cross-functionally: partner with Product, Clinical, and Ops to translate requirements into engineering reality; serve as the technical voice in roadmap conversations—not a scheduler, but a decision-maker
Requirements
- 6+ years of software engineering experience, including 2+ years in a lead or management role
- Hands-on experience with Node.js / TypeScript backends and Next.js / React frontends—read, write, and review production code at a senior level
- Strong database fundamentals: PostgreSQL (schema design, migrations, query optimization), Redis
- Claude Agent SDK: demonstrated experience building and orchestrating multi-agent pipelines—decomposing tasks, defining subagent roles, managing context handoffs, validating agent output
- LLM Integration: production experience integrating LLMs into real systems—streaming, tool use, structured outputs, prompt engineering
- AI Dev Tooling: daily use of Claude Code, Cursor, or equivalent; built workflows around these tools, not just used them ad hoc
- You can articulate—with specificity—how agent orchestration changes what a small engineering team can ship
- Proven experience designing and building web A/B testing platforms from the ground up—not just using third-party tools, but owning the infrastructure
- Deep understanding of experiment design: randomization, assignment consistency, statistical power, holdout groups, and avoiding novelty bias
- Experience running experiments across high-traffic consumer funnels (checkout, onboarding, pricing, landing pages)
- Familiarity with feature flag systems (LaunchDarkly, Statsig, homegrown) and experimentation analytics pipelines
- Hands-on experience building consumer apps and e-commerce platforms end to end—storefronts, checkout, subscriptions, billing
- Built user behavior tracking infrastructure: event schemas, analytics pipelines, conversion funnels, retention analysis
- Familiarity with tools like Segment, Mixpanel, Amplitude, or equivalent homegrown tracking systems
- Experience running engineering sprints, managing dependencies, and owning delivery timelines
- Ability to write engineering specs that AI coding agents and engineers can execute with minimal back-and-forth
- Familiarity with AWS (EC2, RDS, Lambda, S3), Vercel, GitHub Actions, and CI/CD pipelines
- Working knowledge of HIPAA/SOC2 requirements—understand how compliance shapes architecture decisions
- Fluent written and spoken English—all team communication is async in English (Slack, PRs, specs, docs)
Nice to have
- Experience in telehealth, DTC health, or a regulated healthcare environment
- Built an internal agent framework or tooling layer that other engineers on your team used
- Shipped a consumer mobile app with measurable retention and a backend you owned
- Background in distributed systems or real-time infrastructure (WebRTC, event-driven architectures)
- Written a post, given a talk, or built something in public about AI-augmented engineering
Benefits
- Remote-first, globally distributed team
- No bureaucracy—decisions happen fast, priorities evolve
- Small team with enormous leverage: decisions show up in production the same week
- AI as infrastructure, not a feature—operating at the frontier with a team that's already bought in
- Real technical complexity: clinical state machines, real-time patient-provider flows, high-stakes billing, HIPAA
- Direct impact: systems built affect patient outcomes
- Equal opportunity employer: hired based on skills, experience, and alignment with values
Additional details
- This is a player/coach role for an engineer-turned-manager
- You will spend roughly 40% of your time coding and in agent pipelines, and 60% managing, designing systems, and raising the team's AI literacy
- Minimum 4–5 hours daily overlap with US Pacific/Eastern required
- Location: Remote — partial US hours overlap required
- Seniority: Manager — Player/Coach
- This position is open to candidates based in approved locations, depending on the role and business needs
- Qualified applicants will be contacted for next steps
- All team communication is async in English (Slack, PRs, specs, docs)