Gensyn
Applied Machine Learning Researcher
Job Summary
This role involves contributing to cutting-edge research by designing and building large-scale deep learning systems with a focus on modularity, composability, verifiability, and continual learning. The candidate should have a strong background in applied machine learning and experience with distributed model training, working autonomously in an environment that blends research and engineering. Responsibilities include running experiments, maintaining ML frameworks, and collaborating across research and engineering teams. The position offers a fully remote setup, competitive benefits, and opportunities for involvement in innovative machine intelligence protocols.
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
- Contribute to novel research by designing and building massive scale deep learning systems focussing on modularity, composability, verifiability, and continual learning
Responsibilities
- Pursue novel research - building scalable, distributed models over uniquely decentralised and heterogeneous infrastructure
- Partner with both researchers and production engineers to design and run novel experiments, taking research from theory all the way to production
- Own and maintain experimental frameworks and test benchmarks for ML research in uniquely high-scale and decentralised settings
- Follow best practices - build in the open with a keen focus on designing, testing, and documenting your code
Competencies
Must have
- Strong background in applied machine learning / engineering
- Hands-on experience with distributed model training
- 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
- Experience building highly performant, distributed systems
- Demonstrated background with novel research in machine learning
Nice to have
- Strong public presence
- Experience working in a startup environment
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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