CTO

Datacontroller Toogeza Ltd.; United States; On-site; USD 180,000 - 320,000; Full time

Job summary

toogeza is seeking a CTO to lead its engineering vertical for a hybrid AI-driven mobile product. The role focuses on scaling an on-device, video-first stack, memory-augmented AI, and a subscription growth engine.

You will own technology strategy, hiring, and unit economics while aligning with the founder’s vision. The ideal candidate has deep experience in production mobile AI, a track record of building strong teams, and hands-on capability across iOS/Android platforms, with a focus on

Qualifications

• CTO/VP Engineering level experience leading 10+ engineers.
• Production mobile video stack experience on iOS/Android.
• Experience migrating workloads from backend to device with unit-economics impact.
• Subscription growth experience and knowledge of paywalls, attribution, and LTV-based decisions.
• Hands-on with agentic AI, LLMs in production and voice stacks.

Responsibilities

• Own the production product engineering vertical, roadmap, and budget.
• Lead and grow the existing team (6 engineers) and hire 1–2 more in 6 months.
• Oversee on-device/edge improvements, latency, and cost optimization.
• Shape architecture for AI agent memory, RAG, and voice integration.
• Drive strategy for subscription growth and partner stack decisions.

Skills

Leadership

Team management

Product engineering

Strategic planning

Tools

Core Image

Metal

AVFoundation

OpenGL ES

Vulkan

on-device ML

Job 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)




• 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

Mobile video stack

• 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

Who we are looking for

Must have

• 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