Offer summary

Qualifications:

Proven leadership experience in managing research teams., Strong technical background in machine learning or distributed systems., Demonstrated ability to guide research from conception to evaluation., Excellent communication skills for navigating technical and executive discussions..

Key responsibilities:

Define and drive a long-term research agenda aligned with Gensyn’s vision.

Mentor and grow a high-performing team of researchers and engineers.

Collaborate with cross-functional teams to deploy research into real-world applications.

Maintain a strong personal research presence through contributions and publications.

Job description

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

Lead and scale a world-class applied research team building massive-scale deep learning systems. You will work across the boundaries of research, engineering, and systems design to ensure the long-term direction, clarity, and impact of Gensyn’s research efforts — all while remaining deeply technical.

Responsibilities

Define and drive a long-term research agenda aligned with Gensyn’s vision for decentralised machine intelligence
Mentor and grow a high-performing team of researchers and research engineers
Collaborate cross-functionally with systems, infrastructure, ML engineers, and product teams to bring research into real-world deployment
Own research quality and direction — reviewing experimental design, validating results, and ensuring scientific rigor
Maintain a strong personal research presence, contributing code, publications, and perspective
Represent Gensyn in external research communities, helping shape the frontier of distributed and verifiable deep learning

Competencies

Must have

Proven leadership experience leading a research team
Strong technical contributions to open-source or published research in machine learning, distributed systems, or a closely related technical field
Demonstrated ability to guide novel research from conception through implementation and evaluation
Experience recruiting, managing, and retaining top-tier research talent
Experience interfacing with protocol, systems, cryptography, or infrastructure engineering teams
Excellent communication skills — with the ability to navigate between executive-level strategy and deep technical detail
Highly autonomous and self-directed, comfortable operating in uncertainty

Preferred

Public presence in the machine learning, decentralised systems, or research communities
Prior experience building research functions from scratch or scaling small teams

Nice to have

Previous startup or founder 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.
Build and design thinly.

Required profile