CTO
Toogeza
| Company | Toogeza |
| Category | Engineering |
| Location | Europe |
| Remote | On-site (inferred) |
| Employment | Not stated |
| Level | Executive |
| Salary | Not stated by the employer |
| Posted | 15 May 2026 |
| Last verified | 30 Jul 2026 |
| Source | Employer career page (ashby) |
Description
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.
COMPANY OVERVIEW
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.
CURRENT TECHNICAL LANDSCAPE
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.
TEAM
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".
WHY WE ARE HIRING A CTO
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.
WHAT YOU WILL OWN
PRODUCTION PRODUCT
- 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
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)
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