Automation Engineer

⌘About Timeleft

Timeleft is a social app that brings strangers together. We match people into groups of 6 and send them to restaurants, cafés, and bars for dinners, drinks, coffees, and runs. Every week, 150,000+ people use Timeleft across 200+ cities in 52 countries. We're 100 employees — and the product, the operations, and the pace are scaling faster than the team.

That means every department — HR, Support, Ops, Finance, Marketing — is running into the same wall: too much manual work, not enough hands. We need someone to tear those walls down. That's where you come in.

⌘The role

You will sit within the Tech team and serve as the bridge between engineering and every other department at Timeleft. Your job is to identify, design, and ship internal automations that eliminate manual work across the company — HR onboarding flows, support ticket classification, ops reporting pipelines, finance reconciliation, and whatever else is burning time.

This is not a "connect two Zaps together" role. We expect deep, hands-on experience with AI-native development tools — Claude Code, Cursor, MCP servers, agentic workflows, scheduled AI tasks, and live interactive artifacts. You'll build real systems that use LLMs as core infrastructure, not just sprinkle AI on top. You should be someone who has already built things with these tools and can show us what you've shipped.

You'll work at the intersection of vibe-coding, no-code/low-code platforms, and lightweight scripting — building the internal tooling and workflows that make Timeleft run faster. Think: the person every team wants to grab when they realize "there has to be a better way to do this."

⌘Key Responsibilities

AI-native development and agentic automation

• Build and maintain AI-powered workflows using Claude Code, Cowork, MCP servers, and agentic frameworks
• Create custom Skills and plugins that encode repeatable workflows for non-technical teams
• Design live artifacts and interactive dashboards that pull real-time data from internal tools via MCP connectors
• Set up scheduled AI tasks for recurring work — weekly reports, data quality checks, digest generation, Slack summaries
• Evaluate, prototype, and ship LLM-based automations: classification, extraction, summarization, drafting, routing
• Build and connect MCPs to integrate internal systems (Google Workspace, Slack, Notion, Jira, etc.) into AI workflows

Cross-functional automation delivery

• Partner with HR, CX, Ops, Finance, and other teams to identify and prioritize automation opportunities
• Own the full lifecycle: intake → design → build → deploy → maintain
• Examples: automated employee onboarding (Google Workspace provisioning, Slack invites, welcome emails), support ticket auto-classification and routing, automated reporting and alerting

No-code / low-code solutions

• Use platforms like Make (Integromat), Zapier, n8n, or Retool where they're the fastest path to value
• Know when no-code is the right call and when you need to write actual code
• Build internal dashboards and admin tools using low-code frameworks

Vibe-coded applications

• Rapidly prototype and ship internal tools using AI-assisted development (Claude Code, Cursor, Copilot)
• Write Python/TypeScript/SQL scripts for data pipelines, integrations, and automation
• Comfortable with pragmatic code that solves the problem — ship fast, iterate based on feedback, harden what sticks

Documentation and handoff

• Document every automation so non-technical stakeholders can understand what it does, when it runs, and who to contact when it breaks
• Train team members on self-service tools where appropriate

⌘Expected Outcomes

• Automations shipped per quarter: 3–5 meaningful cross-functional automations delivered and running in production
• Time saved: Measurable reduction in manual hours for partner teams (target: 20+ hours/week saved across the org within 6 months)
• Reliability: Automations with clear alerting and fallback paths
• Stakeholder satisfaction: Positive feedback from internal "clients" (HR, CX, Ops) on responsiveness and solution quality

⌘Skills & Competencies

Must have:

• Demonstrated AI tool fluency — hands-on experience building with Claude Code, Cursor, or equivalent AI coding tools. Not "I've tried it" — you should have shipped something real with these tools and be able to walk us through it
• MCP and agentic workflow experience — built or configured MCP servers, connected tools to AI agents, or created custom Skills/plugins for agentic platforms
• Strong scripting in Python or TypeScript — you can architect a working system, not just write one-off scripts
• Experience with at least one no-code/low-code platform (Make, Zapier, n8n, Retool, Bubble)
• Working knowledge of APIs — REST, webhooks, OAuth — and ability to wire up third-party services
• Basic SQL for querying databases and feeding automations
• Proven track record of automations with measured impact — you can point to specific projects and say "this saved X hours/week" or "this reduced error rate by Y%"
• Strong communication skills — you'll spend as much time understanding problems as building solutions

Nice to have:

• Experience building Cowork Skills, scheduled tasks, or live artifacts
• Familiarity with prompt engineering patterns for production use (not just chat)
• Experience with workflow orchestration (Temporal, Airflow, or even well-structured cron)
• Knowledge of Google Cloud Platform, Supabase/PostgreSQL
• Experience building Slack bots, internal chatbots, or AI agents
• Background in process improvement or operations

Soft skills:

• High autonomy — you'll often be the only engineer on a project
• Ability to translate vague "we need this automated" requests into clear requirements
• Bias toward shipping fast over building perfectly
• Comfortable saying "no" to requests that don't justify the effort

⌘Required Experience

• 2–5 years in a role involving automation, internal tooling, integrations, or ops engineering
• A portfolio or examples of automations you've built with measurable outcomes — we want to see what you shipped, what tools you used, and what the before/after looked like. Side projects, freelance work, and internal tools all count
• Active use of AI development tools — you should be building with Claude Code, Cursor, or similar tools today, not planning to learn them on the job
• Experience working cross-functionally with non-technical teams
• Fluency in English required

⌘Objectives for the first 3 months

Month 1: Audit and quick wins

• Meet with every department lead to map current manual processes and pain points
• Deliver 1–2 quick-win automations (the low-hanging fruit that builds trust)
• Get fully onboarded on the internal tech stack (Slack, Notion, etc.)

Month 2: Pipeline and delivery

• Establish a prioritized backlog of automation requests with clear impact estimates
• Ship 2–3 more meaningful automations, including at least one AI-powered workflow
• Set up monitoring/alerting for all deployed automations

Month 3: Process and scale

• Formalize an intake process so teams can request automations in a structured way
• Document all shipped automations
• Present a quarterly automation roadmap to leadership

⌘Recruitment process

Introduction interview (30 min): Talent Acquisition Lead, Maja. We'll talk about your experience, what you're looking for, and whether Timeleft feels like a good fit.

Case study (at home): The assessment to help us evaluate you technical skills and logical thinking. 

Skills Assessment (45 min): You'll meet with you future manager, Misha - Head of Engineering and discuss the test, past automations, portfolio walkthrough, and technical depth. 

Final interview (30 min): You'll meet with a non-technical stakeholder to assess how well you communicate with potential stakeholders and you understanding of our needs.