Product Engineer (Python)

Product Engineer — ZenML & Kitaru

Overview: The Fastest Python Engineer We Can Find

We’re hiring a Product Engineer to help us build the tools Python developers will use to ship AI in production for the next decade.

One line version: we want someone who ships high-quality Python code fast. Not sloppy-fast. Actually-fast. The kind of engineer who turns a Slack conversation into a merged PR before lunch, reads every line of what shipped, and writes code other engineers are relieved to inherit.

You’ll work across our two open-source products — ZenML, the AI platform thousands of teams use to orchestrate ML pipelines and agent workflows, and Kitaru, our brand-new durable execution layer for Python agents (crash recovery, human-in-the-loop, replay from any checkpoint). Both are Python-first. Both are Apache 2.0. Both are moving fast. Where you spend your week depends on where the highest-leverage problem is that week.

This is not a backend role. It’s not a frontend role. It’s a product role that happens to require a serious engineer. You’ll talk to users, write the Python, touch the TypeScript dashboard when it matters, ship it, measure it, and come back with the next iteration before anyone’s had time to form an opinion on the first one.

We expect you to be AI-pilled — coding agents are already part of how you work, and you have strong opinions about where they help and where they don’t. But AI-pilled ? vibes-pilled. You read every diff. You can defend every line. You’d be a strong engineer even if every AI tool disappeared tomorrow; the AI just makes you faster.

Key Responsibilities (The “Jobs to be Done”)

• Ship Features End-to-End, Fast: Take problems from user conversation to shipped code in days, not weeks. Own the full loop — design, implementation, tests, docs, rollout — across ZenML, Kitaru, or both. Examples of what you might ship in your first months: a new framework adapter for Kitaru (e.g., CrewAI, LlamaIndex), a new orchestrator integration for ZenML, a rewrite of the onboarding flow that cuts time-to-first-pipeline in half, a replay debugger that agent developers will talk about on Twitter.
• Build for Python Developers, as a Python Developer: Our users are Python devs building pipelines and agents. You are one of them. Every API choice, every error message, every example reflects what feels right when you’re the one writing the import statement at 11pm.
• Extend the Core Primitives: Evolve ZenML’s @pipeline/@step and Kitaru’s @flow/@checkpoint models — new execution semantics, retries, fan-out, sandboxes, framework adapters (PydanticAI, OpenAI SDK, Claude Agents SDK).
• Close the Loop on UX: Jump into the TypeScript/React dashboards when a feature needs a surface. Fix the onboarding friction you just noticed. Don’t file a ticket — open a PR.
• AI-First, Craft-First: Coding agents are a daily tool, not a novelty. You also read every diff, understand what shipped, and don’t merge anything you couldn’t explain in a code review. Build internal tooling (often using Kitaru itself) that makes the whole team faster.
• User-Driven Iteration: Live in our Slack and GitHub. Turn user confusion into a PR the same day. Your product instincts matter as much as your engineering ones — often more.
• Raise the Bar: The codebase is better after you touch it. You leave behind better tests, clearer interfaces, sharper docs. You make the engineers around you better, not just your own output.

Tech You’ll Work With

• The Core: Python 3.11+, Pydantic, SQLAlchemy/SQLModel, FastAPI, asyncio
• Our Products: ZenML (pipelines, stacks, artifacts, orchestrators) and Kitaru (durable flows, checkpoints, replay, human-in-the-loop)
• The AI/Agent Ecosystem: PydanticAI, OpenAI SDK, Claude Agents SDK — you know where each one hurts and where our tools help
• The Frontend (bonus): TypeScript, React, Tailwind — for when a feature needs a dashboard surface
• The Infra: Docker, Kubernetes concepts, SQL, cloud deploys (AWS, GCP, Azure)
• The AI Coding Stack: Claude Code, Cursor, Codex — whatever makes you ship 3x faster. We expect you to use these; we don’t expect you to apologize for them.

What We’re Looking For

• The Python Shipper: 4+ years writing production Python. You’ve built libraries, SDKs, or developer-facing APIs that real people rely on. Your code reads like someone gave a damn.
• Sharp and Decisive: You grasp a problem quickly, reduce it to its essence, and start coding. You don’t need three design docs to ship a feature. End-to-end in days — not weeks — is your natural cadence, not your stretch goal.
• AI-Pilled, Not Vibes-Pilled: Coding agents are already your co-pilots. You know when to let them drive and when to grab the wheel. You still write clean, tested, maintainable code, and you’d be a strong engineer even if every AI tool disappeared tomorrow. The AI is a force multiplier on craft, not a replacement for it.
• Product Taste for Developer Tools: You feel physical pain when an API is clunky. You’ve used tools that made you love programming and tools that made you quit. You know what good looks like — and you can describe why.
• Builder, Not a Specialist: You don’t care whether a task is “backend” or “frontend” — you care whether it ships. TypeScript/React knowledge is a real plus; willingness to learn it fast is a requirement.
• Agent- or ML-Curious: Bonus if you’ve built agents or ML pipelines yourself (hobby, side project, past role) and have real opinions about what breaks in production. If you’ve hit a “my agent crashed at step 6 and now I need to restart everything” moment — you already get Kitaru.
• Low Ego, High Standards: You’re direct in code reviews and kind in conversation. You’d rather be corrected than wrong.

This Role Is Probably Not For You If…

• You need a detailed ticket before you can start working.
• You want to specialize deeply in one narrow slice of the stack.
• “Ship in days” sounds reckless rather than exciting.
• You see AI coding tools as a threat to your identity as an engineer.
• You’d rather debate a design for two weeks than prototype it in two days.

(If any of these describe you, we genuinely think you’d be happier somewhere else — and we’d rather tell you upfront than waste your time.)

Why This Role Matters

• Two Products, One Mission: ZenML has years of production trust with enterprise ML teams. Kitaru just launched and is defining a new category in agent infrastructure. You’ll move between them based on where your impact is highest — and help shape how Python developers build AI, full stop.
• Ground Floor, Real Traction: Kitaru is new, but ZenML isn’t — we have thousands of teams running our tools in production and a warm, active open-source community that tells us what to build next. You get greenfield energy and real users from day one.
• Velocity as a Feature: We hire Product Engineers because the AI space is moving fast and the team that ships fastest wins. You won’t be told to slow down. You’ll be expected — and trusted — to go faster.
• Open Source Reputation: Both products are Apache 2.0. Every PR you merge lands in the hands of thousands of developers. Your GitHub profile will do the talking at your next job (though we hope you stay).
• Use Your Own Products: Build ZenML and Kitaru. Then use ZenML and Kitaru to build ZenML and Kitaru. The tightest possible dogfooding loop, on an AI-first team that treats great tooling as an advantage — not an afterthought.
• Small Team, Real Ownership: No layers, no politics, no waiting for three approvals. The founders are engineers, the engineers talk to users, and nobody’s hiding behind Jira.