Head of Engineering (AI)

Unimatch Lab is an AI-driven venture studio from Silicon Valley.

We are building our own AI technology stack and a portfolio of AI-driven assets: from consumer AI products and smart devices to on-prem LLM clusters and foundational layers (memory layer, RAM/VRAM optimization, orchestration, AI software for infrastructure and compute).

This includes R&D in local, distributed, and orbital data centers and distributed computing.

Goal: join the world’s top 50 AI companies with a combined asset valuation of $10B+ by 2032.

We are an OKR-driven company: measurable outcomes, speed, transparency, and ownership.

We hire A-players: autonomous, fast, strong execution, high accountability.

Position summary

You own engineering for the Unimatch Lab holding: technical leads, product CTOs, engineers, and infrastructure. You are the single technical gate for how the holding builds and ships.

This is a partner track, not a hired-hours role. You take the full technology domain, make it measurable, and run it without being told what to do next.

In the first year you set up the development system (planning, backlog, reporting, metrics), hire and shape the team and compensation model, set shared engineering standards across the portfolio, and take the AI-agent orchestration architecture to production.

You keep a helicopter view and stay hands-on: you know how to build and run multi-product development through cross-functional squads, and you can still ship a prototype, stand up Kubernetes, or debug a hard technical problem yourself.

Key responsibilities

Engineering system across the holding

• Own how the holding builds and ships: you are the single technical gate for technical leads, product CTOs, engineers, and infrastructure
• Stand up the development system for the first year: planning, backlog, reporting, and metrics, so delivery is transparent and forecastable
• Make the same workflow usable by any product in the portfolio, not a one-off process for a single team
• Keep dozens of parallel streams in control: prioritize, track, and close them to a result without losing the helicopter view

Team, org, and compensation

• Build the engineering organization from the current base: hire, structure squads and cross-functional teams, and set who owns what
• Design and run the compensation model for engineering so it matches holding scale, not a single-product startup
• Run daily management and tracking: what is in flight, what is blocked, what ships, what waits
• Shape the org so the team can grow without you managing every thread by hand

Standards and technical judgment

• Set shared engineering standards for all products in the portfolio and keep them in use, including how work is planned, reviewed, and shipped
• Decide architecture trade-offs at holding level: stack, patterns, constraints of hardware, memory, and model architecture, not only LLM tooling
• Unblock production problems yourself when the system needs it: prototype, Kubernetes, debug, ship

AI-agent orchestration

• Take the AI-agent orchestration architecture the holding runs on from design to production
• Own the engineering of agent pipelines, harnesses, and wrappers as a production system, not a research demo

Required qualifications

• Experience building and running engineering at scale: tens of people and several products, including from scratch
• Deep AI background: LLMs, AI-pipeline orchestration, architectures and their limits (hardware, memory, transformer math, RL); background from DevOps through ML
• Full startup cycle: idea -> PMF -> rounds -> growth
• STEM or engineering degree
• Fluent English
• Already in the United States, or ready to relocate soon
• Can hold many parallel streams, work with large data, and still ship hands-on (prototype, Kubernetes, production debug)
• Organizational management, daily tracking, and hiring: strong engineers choose to work with you; you think in product and business, not only in code

Preferred qualifications

• Big-tech experience
• Hardware and devices; deep work in TTS/STT and conversational AI
• Agent harnesses, wrappers, and orchestration of AI agents
• Startup path through IPO
• Top-tier university

Technology stack

• LLM, transformer architectures, RL
• AI-pipeline and AI-agent orchestration
• Constraints at hardware, memory, and architecture level
• Kubernetes and production infrastructure
• DevOps through ML

Employment terms

• Format: Full-time, remote; sync windows by agreement (CET and other zones as needed)
• Location: Europe, MENA, LATAM, USA
• Compensation: Up to 15000 USDT total (base fix + shipment bonuses)
• Equity: Option or equity in products you create and lead
• Venture studio: Option to launch your own startup inside the venture studio - with our infrastructure, team, funding, and network
• Network: Work with top venture funds from Silicon Valley and a strong professional team
• AI exposure: Portfolio of AI products in production; regular exposure to leading AI solutions shipped to production