Toogeza
CTO
1mo ago
WorldwideC-leveliosandroidaivoice controlbackendfullstack
CTO role for an AI-native consumer mobile company developing an AI-driven video editing app with voice control.
Requirements
- 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
Other
- We are toogeza , a Ukrainian recruiting company that is focused on hiring talents and building teams for tech startups worldwide. People make a difference in the big game, we may help to find the right ones. Currently, we are looking for a CTO to join one of Toogeza’s clients.
- 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.
- We build a full-stack product: the AI agent is the core, with mobile apps, performance marketing, growth, and product analytics built around it. Industry references — Lightricks, Bending Spoons, Codeway, Photoroom: subscription mobile publishers with in-house R&D and mature growth infrastructure.
- 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.
- The engineering team today is 6 people, all reporting directly to the CTO:
- AI / Agent: 2 people (including a recently hired AI Engineer)
- iOS: 2 engineers
- Android: 1 engineer
- Backend: 1 engineer
- Plus product, marketing, and growth. The plan for engineering team growth is targeted — +1–2 people over 6 months. This is not "build a team from scratch" — it is "lead an existing team and reinforce it where needed".
- 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.
- Full technical ownership of the live iOS app and the Android launch
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- What’s next? If this role sounds like a fit — we’d love to hear from you! Just send over your CV and anything else you’d like us to consider.
- We’ll review everything within five working days , and if your background matches what we’re looking for, we’ll get in touch to set up a call and get to know each other better.