CTO

The architecture is hybrid: AI video analysis runs server-side, the final clip assembly happens on-device. A strategic direction the CTO will own - gradually migrate decisions from backend to device without quality loss, to improve unit economics.

The voice stack is built on third-party providers (Whisper / Deepgram / OpenAI Realtime / ElevenLabs). The CTO must evaluate trade-offs between providers across latency, cost, and quality, and decide when to replace or hybridize parts of the stack.

The LLM agent (Operator / Director / Producer hierarchy) uses a memory system on pgvector + Neo4j (GraphRAG hybrid retrieval). The user portrait is built from multi-signal data - camera, voice, gallery, social analytics., Our product is entering a phase where the engineering team must become a full product engineering vertical with its own tempo, processes, and culture. We need a CTO who simultaneously owns three intersections:

• Production mobile video stack at the level of Core Image / Metal / OpenGL ES / AVFoundation, video processing, rendering, on-device inference - real experience, not theory
• Agentic AI and LLMs in production - LLM orchestration, RAG/GraphRAG, agentic architectures, memory systems, evaluation frameworks, hands-on with voice stacks
• Subscription growth infrastructure at Codeway / Bending Spoons / Lightricks level - paywall A/B, MMP attribution, SKAN/AdAttributionKit, LTV/ROAS pipelines, cost optimization

This is the owner of the product engineering vertical, with direct accountability for the production product, hiring, unit economics, and technical strategy., * Auto-edit quality and stability: scene understanding, shot selection, beat matching, subtitle generation, thematic coherence

• Voice agent: latency, recognition accuracy, dialog quality, selection and optimization of third-party providers (Whisper / Deepgram / OpenAI Realtime / ElevenLabs)
• Hybrid pipeline: video analysis server-side, assembly on-device - and the strategic shift toward more on-device without quality loss
• Operational excellence: uptime, incident management, AI output quality monitoring

Unit economics and cost optimization

• Migrating tasks from backend to device as the key unit-economics lever - prioritization, technical plan, quality metrics during migration
• GPU inference and server-side video pipeline optimization (FFmpeg, queues, CDN)
• Cost management for third-party AI APIs (LLM, ASR, TTS, music generation/licensing) - hybrid of in-house and third-party
• Cost-aware decisions across cloud, third-party SDKs, infrastructure

Engineering organization

• Manage the current engineering team (6 people: 2 AI, 2 iOS, 1 Android, 1 backend)
• Targeted hiring of 1-2 people over 6 months to address specific tasks and stack gaps
• Performance reviews, technical onboarding, engineering culture
• Process design - sprint cadence, code review, on-call, incident management - proportionate to stage, without premature process overhead

Agent architecture and platform

• Evolve product’s agentic architecture (Operator / Director / Producer hierarchy)
• Memory system on pgvector + Neo4j (GraphRAG hybrid retrieval), Knowledge Graph, user portrait pipeline
• Voice-first interface - optimize end-to-end latency and cost through provider selection and stack hybridization
• Evaluation infrastructure for LLM agents - LLM-as-judge, regression tests for edit quality and tone of voice, * iOS: Swift, Core Image, Metal, AVFoundation, Vision; on-device CoreML inference where it improves unit economics
• Android (in development): Kotlin, OpenGL ES / Vulkan, MediaCodec, CameraX, ML Kit / TensorFlow Lite
• Server-side rendering pipeline (FFmpeg, GPU inference), CDN, video delivery
• On-device video composition, real-time preview, optimization across device classes

Growth infrastructure and unit economics

• Subscription stack: RevenueCat / Adapty, paywall A/B testing, server-driven paywalls
• Attribution: AppsFlyer / Adjust / Branch, SKAN / AdAttributionKit, probabilistic modeling, web-to-app funnels
• Product analytics and data warehouse: event pipeline, BI, cohort analysis, LTV/ROAS dashboards

Strategy and investor readiness

• Translate company strategy into a roadmap: from tactical quick wins to stable long-term
• Tech due diligence readiness for the next round
• Direct partner to the founder on product and business decisions

Do you have experience in iOS?, * CTO / VP Engineering / Head of Engineering experience in a consumer mobile subscription company. Experience leading a team of 10+ engineers is required; companies of 30+ people - strong plus

• Production video/photo stack: Core Image, Metal, OpenGL ES / Vulkan, AVFoundation, MediaCodec - both mobile platforms
• Experience migrating workloads from backend to device (on-device inference, on-device video processing) with measurable unit-economics impact
• Subscription growth experience at the level of Codeway / Bending Spoons / Lightricks / Photoroom - paywall optimization, MMP attribution, post-ATT/SKAN, LTV-driven decisions
• Agentic AI / LLMs in production: you understand the difference between “calling the OpenAI API” and building an agent system with memory, tools, and an eval loop. Hands-on experience with voice stacks (ASR/TTS, real-time voice agents) - required
• Management maturity: hiring, performance reviews, budgeting, cost-aware decisions, business orientation

Strong plus

• Production launch experience with an auto-edit video or voice-controlled app (Captions / Descript / Opus Clip / Submagic / CapCut-class)
• Proprietary ML models in production - fine-tuning, on-device CoreML/TFLite, model distillation
• Experience optimizing third-party AI API cost and latency (LLM, ASR, TTS) through hybridization and in-house replacements

AI-native consumer mobile company with a subscription monetization model. Our iOS app is live: an AI agent with voice control that automatically assembles a finished video clip with music and subtitles from multiple videos a user uploads from their camera roll. The agent determines the theme, selects shots, edits, and adds audio. Android is in active development.