Machine intelligence will soon take over humanity’s role in knowledge-keeping and creation. What started in the mid-1990s as the gradual off-loading of knowledge and decision making to search engines will be rapidly replaced by vast neural networks - with all knowledge compressed into their artificial neurons. Unlike organic life, machine intelligence, built within silicon, needs protocols to coordinate and grow. And, like nature, these protocols should be open, permissionless, and neutral. Starting with compute hardware, the Gensyn protocol networks together the core resources required for machine intelligence to flourish alongside human intelligence.

The Role

We’re looking for a Technical Product Manager to own Gensyn’s core infrastructure systems - foundational primitives such as REE (verifiable execution of AI/ML workloads) and AXL (p2p networking for AI agents) along with the execution, coordination, and training layers that power our decentralized AI protocol.

This is a 0–1 role. You will shape low-level infrastructure, define developer-facing surfaces, and help identify how our tech reaches real product-market fit.

Responsibilities

Own product direction and delivery for core infrastructure systems.
Partner with engineering and research to shape complex systems into usable products.
Translate distributed systems and ML workflows into clear developer-facing abstractions.
Define APIs, SDKs, and workflows that enable others to build on the protocol.
Engage directly with technical users (ML Engineers & Researchers) to identify friction and improve usability.
Identify and validate early product-market fit through use cases and design partners.
Connect technical capabilities to real-world and commercial outcomes.
Define and track metrics for adoption, reliability, and developer satisfaction.

Competencies

Must have

Experience in early-stage or high-growth startups with high ownership and ambiguity.
Proven ability to drive product delivery across technical teams, especially in infrastructure, platform, or developer-facing systems.
Strong technical communication skills and the ability to translate complex systems into clear product direction and user value.
Background in domains such as distributed systems, compute platforms, compilers, or APIs/SDKs.
Experience building for technical users (engineers, developers, researchers), with strong empathy for developer workflows and integration pain points.
Ability to connect technical systems to real-world use cases and commercial outcomes.

Preferred

Experience with AI coding/prototyping tools (e.g., Cursor, Codex, Claude Code, GitHub Copilot).
Experience owning core infrastructure or developer platforms end-to-end.
Experience working in research-heavy or AI/ML-driven environments.
Experience taking technically novel systems toward product-market fit.
Exposure to decentralized or protocol-based systems.

Nice to have

Hands-on programming experience (Python, Go, Rust, or similar).
Open-source contribution or community experience.

Compensation / Benefits

Competitive salary + share of equity and token pool
Fully remote work - we currently hire between the West Coast (PT) and Central Europe (CET) time zones
Visa sponsorship - available for those who would like to relocate to the US after being hired
3-4x all expenses paid company retreats around the world, per year
Whatever equipment you need
Paid sick leave and flexible vacation
Company-sponsored health, vision, and dental insurance - including spouse/dependents [🇺🇸 only]

Our Principles

Autonomy & Independence

Don’t ask for permission - we have a constraint culture, not a permission culture.
Claim ownership of any work stream and set its goals/deadlines, rather than waiting to be assigned work or relying on job specs.
Push & pull context on your work rather than waiting for information from others and assuming people know what you’re doing.
Communicate to be understood rather than pushing out information and expecting others to work to understand it.
Stay a small team - misalignment and politics scale super-linearly with team size. Small protocol teams rival much larger traditional teams.

Rejection of mediocrity & high performance

Give direct feedback to everyone immediately - rather than avoiding unpopularity, expecting things to improve naturally, or trading short-term pain for extreme long-term pain.
Embrace an extreme learning rate - rather than assuming limits to your ability / knowledge.
Don’t quit - push to the final outcome, despite any barriers.
Be anti-fragile - balance short-term risk for long-term outcomes.
Reject waste - guard the company’s time, rather than wasting it in meetings without clear purpose/focus, or bikeshedding.

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