Gensyn
Research Manager
Job Summary
The role involves leading and scaling a research team focused on building large-scale deep learning systems within the field of decentralized machine intelligence. The candidate will define research strategies, mentor team members, and collaborate across engineering and product teams to translate research into real-world applications. A strong background in research, system design, and leadership in machine learning or distributed systems is required. The position offers remote work, competitive benefits, and opportunities for visibility in the research community.
Required Skills
Benefits
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
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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.
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