Job Description

• Full-Time
• Remote
• 6.400 USD

About Company

Company is an AI-native distribution company for creators and media IP. We're building a platform that takes raw content, decides what to post, where, and when, renders it, and learns from what performs — think Palantir for social media and the creator economy.

We're organized around content verticals (video, podcasts, and more to come). Each vertical plugs into the same core platform: a unified content and performance graph that gets smarter with every campaign we run. The team is small, senior, and moves fast — YC-style shipping cadence with Palantir-style rigor on systems and data.

The role

We're hiring a Product Engineer to build out our podcast vertical on top of the core platform.

Podcasts are a strategic bet for us, and this is a greenfield vertical with real surface area: automated generation pipelines, AI-first editorial experimentation, and the data layer that lets us learn from every episode we ship.

You'll work closely and daily with our Head of Product to shape what we build, but the engineering execution is yours. You'll turn product direction into shipped software — user flows, MVPs, backend and frontend implementation, and the scaling work that comes after an experiment starts working. You'll also be the person making sure the podcast vertical feeds cleanly into the core platform's data model, rather than growing as a silo.

This isn't a role where you wait for specs. You'll shape them alongside product, and then you'll build them.

What you'll actually work on

• Automated podcast generation pipelines. Script → voice → edit → publish, powered by whichever LLM / TTS / audio stack fits the job. You'll own the architecture.
• AI-first podcast experimentation. Fast editorial experiments — formats, hosts, lengths, distribution patterns — instrumented so we actually learn from them.
• Contributing to the core platform. The podcast vertical is one piece of a larger system. You'll extend shared infrastructure (content models, job orchestration, posting, analytics) rather than building parallel stacks.
• Shipping MVPs in weeks, not quarters. Proposal → skeleton → production. Tight loop with product and the rest of engineering.
• Scaling what works. When an experiment hits, you're also the person thinking about reliability, cost, and how to 10x the volume without rewriting it.

Who succeeds here

You're a generalist engineer with strong product instincts. Comfortable in Python (our backbone) and TypeScript/React (our frontends), and opinionated about data. You've shipped things that run unattended and know the difference between "works on my machine" and "runs in production."

You love collaborating with product. You're not trying to be the PM — you're trying to be the engineer who makes the PM's job easy. You push back thoughtfully, surface tradeoffs early, and ship things that actually match the intent behind the spec.

You have real taste for online media. You listen to podcasts actively — not as research, because you like them. You have opinions about what makes an episode work, who the interesting hosts are, what YouTube-native media looks like versus traditional audio, and where AI is going to break this open (and where it won't).

You are culturally fluent in US content. Native-level English. You read The Ringer, Semafor, Puck, or whatever's replaced them this year. You know the difference between a Joe Rogan clip and a Lex Fridman clip without being told.

You think in MVPs but build for scale. You know when to stub and when to structure. You don't over-engineer, but you also don't ship things that will have to be thrown away in six weeks.

You're high-agency. You write the proposal before the code, state your assumptions, and proceed. You don't wait to be unblocked — you unblock yourself and flag tradeoffs clearly.

Our stack

• Backend: Python, Postgres, Google Cloud Storage
• Frontend: TypeScript, React
• Infra: Railway
• AI: We work across the major frontier models (Anthropic, OpenAI, Google) and keep the stack model-agnostic. We expect you to have real opinions about which model fits which job.
• Tools: Claude Code as our primary agent interface. GitHub with branch protection and automated review. Notion for docs. Slack for comms.

How we work

• Proposal before code. One-pager before any meaningful build — applies to everyone.
• Assumptions, not questions. State what you're assuming and proceed. Only escalate when the answer materially changes architecture, revenue, or risk.
• Written async by default. Fast pings in chat, PRs and docs for everything that matters.
• Small team, wide scope. You'll work directly with the Head of Product and a senior engineering team. Your impact is legible.

Nice to have

• Experience at an AI-native startup, content platform, or media/editorial tooling company
• Prior work with audio pipelines (ASR, TTS, editing, publishing)
• A personal project you'll want to show us — ideally something in media, content, or automation

What we offer

• Competitive cash
• Close collaboration with a product-led founding team
• A senior team that takes engineering and editorial craft seriously
• Remote, US hours

Process

1. Intro call with the recruiter
2. 45-min conversation with the Head of Product + Eng Teammate
3. A paid work trial (1–2 weeks, part-time compatible) on a real problem in the podcast vertical
4. Offer