Gensyn
Machine Learning Researcher
Job Summary
This role involves leading cutting-edge research in deep learning, focusing on scalable and modular neural network architectures, verifiability, and continual learning. The candidate will publish in top-tier AI conferences, collaborate with research and engineering teams, and contribute to innovative machine intelligence models. The position emphasizes high autonomy, self-motivation, and experience with distributed systems and research publication. Benefits include competitive salary, equity, remote work options, and comprehensive health coverage.
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
- Own and pursue novel research - building scalable, distributed models over uniquely decentralised and heterogeneous infrastructure
Responsibilities
- Publish novel research in deep learning focussing on massive scale modular/composable architectures, verifiability, and continual learning
- Research novel model architectures - design, build, test, and iterate over completely new ways of building neural networks
- Publish & collaborate - write research papers targeting top-tier AI conferences such as NeurIPS, ICML, ICLR and collaborate with experts from universities and research institutes
- Partner closely with research and production engineering teams to experiment and productionise the research
Competencies
Must have
- Demonstrated background with novel research in machine learning via first author publications
- Comfortable working in an applied research environment with extremely high autonomy and unpredictable timelines
- Highly self-motivated with excellent verbal and written communication skills
Preferred
- Existing work in modular, highly distributed, or continual deep learning
Nice to have
- Experience with cryptography applied to machine learning
- Strong public presence
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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