Mappa
Product Engineer — AI Podcasts
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Product Engineer (Podcasts) – Full-Time, Remote
Role: Full-Time | Remote | $6,000/month
About the Company
We’re an AI-native distribution company for creators and media IP, building a platform that handles content lifecycle across formats—video, podcasts, and beyond. Our ecosystem automates what-to-publish, where, when, and leverages real-time learnings to optimise performance.
Organized vertically, each team (video, podcasts, etc.) feeds into a unified content and performance graph that evolves with every shipped campaign. Think Palantir for social media and the creator economy—small, senior, fast-moving, and rigor-driven.
The Role
We’re hiring a Product Engineer to launch our podcast vertical—a greenfield project with rich opportunities:
- End-to-end automation: Script writing → AI voice generation → editing → publishing, sculpted by frontier models (TTS, LLMs).
- AI-powered experimentation: Rapid tests on formats, scripts, hosts, lengths, and distribution, with analytical rigor.
- Seamless integration: Ensuring the vertical feeds into the core platform’s architecture, not as a silo.
- Unsoning thinking: Shape specs alongside product, then execute swiftly—from MVP-to-scale in stitches (weeks, not quarters).
This is not a "write specs then deliver" role. You’ll propose, build, iterate, and own impact.
What You’ll Work On
- Automated generation pipelines Define and excute end-to-end workflows (script → voice → edit → publish) using Python/TTS/AI tools. Own the tech stack.
- AI-first editorial experimentation Instrumentalise every variable—format, length, voiceover, guest dynamism—to learn in real time.
- Core platform extension Scale the shared data model, job orchestration, and analytics. Avoid parallel stacks; own vertical integration.
- Rapid shipping cadence Proposal → Skeleton → Production loop. MVPs first, not scalable before proven}.
- Scaling reliability & volume When an experiment works, make it idiot-proof and cost-effective—then automate.
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Who Succeeds Here
Core Traits
- Tech/product hybrid: Fluency in product design + engineering execution. Python (backend) + TypeScript/React.
- Ownership mindset: Ship systems evaluation-ready (works like "Production" across environments). Hate "works on my machine".
- Product intuition: Collaborate tightly with product—not to usurp, to elevate. Pushback thoughtfully, surfacing tradeoffs early.
- Media passion: Listen intentionally to podcasts—both traditional and new media (Twitch/Youtube). Opionated curator.
- Knowledge of key hosts, formats, and AI's edge cases.
- Seasoned subculture housing (substack/etc.), and the feast-after-feed. Cognisant of nuances in ed共有and voice attractiveness.
- Ships MVP mindset: Structured architecture without over-abstracting. Avoid premature scaling.
- Stubs before النصف-absolute solutions —but never build to discard in 60 days.
- High agency: Falling step to propose dependencies and unblock yourself. Assume accountability, clarify assumptions early.
Tech Fluent
- Hands-on with Python (network) and TypeScript/React
- Good with data principles (structure queries, timeseries optimization).
- Seamlessly compose frontier models (Anthropic, OpenAI, Google) to tackle role-specific tasks.
**Nice:
- AI/Audio exposure: Tasks done in ASR, TTS, editing code, digital marketing distribution.
- Content projects: Music, podcasts, subtitles, or fully AI-assisted domains.
- Fall-line writing: Things you built, for interview prep, preferably in media production.
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Our Stack
- Basecamw: Python, Posgres, GCS.
- Front: TypeScript + React.
- Infrastructure: Railway
- AI/ML: Model-agnostic stack of challenge techniques.
Leverages frontier models but abstracts propagation.
- Desired: Opinions on choice (or set) for each pipeline.
- Tools:
- Code: GitHub w/ protection + review.
- Docs: Notion
- Comms: Slack.
How We Work
- Proposal-first: One-pager shared prior to code. No unexpected feature bloat—assumptions logged, decisions in-team.
- A Assuming over asking: Make critiques visible state what you’re assuming, then execute—and flag shifts only when data, pricing, or risk shift the playbook.
- Synchronous done verbally or through PR//exchange, written action items to settle issues.
- Small team, big impact: Talk directly with the Head of Product and a senior engineering peers route. Impact is transparent early.
What We Offer
- Competitive stipend: $6,000/month upon full role.
- Close partnership: Product-led, engineering-driven.
- +2 weeks of on-site work trial: Paid and flexible, on context-changing real problems.
- USA hours / remote culture.
- Elevated editorial craft: Respect for both data and taste.
Process
- Intuition with fit consultant text.
- 45-minute chat (Head of Product & peer).
- Paid work trial—per part-time- 1-2 weeks on a concrete podcast-related problem.
- Decision and profile.
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