The role involves building AI-ready web applications, collaborating with product managers and designers, and improving workflows based on user feedback.
The role
We're hiring a Staff Engineer to own the architectural layer that makes this real, working alongside our engineers, PMs, and designers shipping AI-first product features end-to-end. You'd ship code, set technical direction, and shape decisions about what's worth building. Your north star here will be: get from idea to validated learning twice as fast.
This role is hybrid in Prague or Brno, or remote within the EU.
How we workWe've moved away from process and toward judgment. The shift looks like this:
- One named driver per initiative. End-to-end ownership of the outcome, the path, the decisions, and the learnings. No approval chains. The driver decides; leadership unblocks.
- One-page pre-reads as the unit of decision. Big work starts with a brief covering the problem, expected outcome, risks, and effort. Leadership reads it and decides. No 30-slide decks. No two-week alignment cycles.
- Continuous delivery, no quarterly planning. Roadmap committed one month out. Beyond that, AI moves too fast for longer cycles to mean anything. We ship to internal first, then beta, then GA. Validation comes from real usage.
- PMs and designers ship to production. Not just specs and Figma. They prototype with AI tools and ship alongside engineers. PMs own what gets built and when. Engineers own how.
If you've been a founding engineer or founder, this should feel familiar. If you've been waiting for a place that operates like that at scale, this is one.
The system around youWe invest before we expect. We are actively looking for the next set of constraints to remove before they slow us down.
- Best AI tools from day one. Cursor, Claude Code, Codex, Glean. No waiting list, no approval process. If a better tool shows up tomorrow, you get that one too.
- AI Champions embedded on every team. Engineers (not coordinators) who pair with you, unblock you, and help you move faster with agents. Four hours every week dedicated to team enablement.
- A codebase built for agents. Curated AGENTS.md files, repo-versioned skills, clean contracts. Continuously evaluated, not accumulating.
- Ship It with AI days. Two days every six weeks. No meetings for ICs. Pick a real problem, try a new AI workflow, ship to production within 48 hours.
- Knowledge that compounds. Monthly engineer-to-engineer events where we share what we're experimenting with, learning, and shipping with AI.
You ship with AI every day. You think in outcomes, not output. You can name a tradeoff you made between scope and quality and tell us why you made it. You can describe what you've stopped doing because AI made it unnecessary. You've owned features end-to-end and know what it costs to get something to GA.
Concretely, we're looking for:
- 6-10+ years of production software engineering experience
- Strong backend skills in Python, Kotlin, or Java, with experience evolving service-level logic and infrastructure
- Hands-on LLM experience in real products: prompt design, context management, evaluation, real understanding of trade-offs (hallucinations, latency, cost, reliability)
- Comfort with distributed systems and event-driven architectures (queues, async processing, service-to-service communication)
- Daily use of AI coding tools as a core part of your workflow — pushing them, refining prompts, knowing where they break
Strong fit: Former founding engineer or founder. A 0-to-1 engineer who turns business insights into prototypes to validate ideas quickly. Someone who's built agentic systems in production, or done deep work with multi-step LLM workflows (tool use, memory, orchestration).
Our tech stack- AI layer: Python, Pydantic AI, Braintrust
- Frontend: TypeScript, React, Relay, GraphQL
- Backend: Kotlin, Ruby (legacy services we're modernizing), with new services built in Kotlin
- Storage: PostgreSQL, MongoDB, Elastic, Redis
- Data pipeline: Python, Keboola, Looker, Snowflake
- Infrastructure: AWS, Cloudflare, Kubernetes, Terraform
Productboard San Francisco, California, USA Office
333 Bush Street, San Francisco, CA, United States, 94104
Similar Jobs
Big Data • Food • Hardware • Machine Learning • Retail • Automation • Manufacturing
Lead program-level change strategy, readiness framework, and QA for change deliverables. Standardize key user learning journeys, manage the integrated change plan, oversee risks and issues, direct Functional Change Leads, represent change at leadership forums, and build lasting organizational change capability.
Top Skills:
ConfluenceJIRAMicrosoft TeamsMs ProjectO9OracleSalesforceSAPSharepointSmartsheet
Artificial Intelligence • Productivity • Software • Automation
The Manager, Sales Assist will lead a team to drive sales and customer growth, coach representatives, and engage with clients using data-driven strategies.
Top Skills:
GongHubspot
Information Technology
As a Senior Backend Engineer at DuckDuckGo, you'll lead backend projects, mentor engineers, and develop AI-enhanced features for the company's privacy-centric product line.
Top Skills:
Ai ToolingGoNode.jsPerlRag Pipelines
What you need to know about the San Francisco Tech Scene
San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine
