Offer summary

Qualifications:

Currently enrolled in a PhD program in Computer Science, Machine Learning, or a related field, or a Master's program in exceptional cases., Prior experience in original research with authorship on ML papers is preferred., Strong understanding of deep learning fundamentals and experience with frameworks like PyTorch, JAX, or TensorFlow., Excellent written and verbal communication skills, with a self-directed and curious mindset..

Key responsabilities:

Contribute to original research in deep learning focusing on modular architectures and verifiability.

Design and prototype novel neural network architectures for decentralized compute environments.

Collaborate on joint publications and projects with academic and industry researchers targeting top-tier AI venues.

Explore new methods for building and verifying neural networks across decentralized topologies.

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

As a Research Intern, you will contribute to cutting-edge research in scalable, distributed machine learning systems. Working alongside experienced researchers and engineers, you'll explore new ways of building and verifying neural networks that operate across huge, decentralised, topologies of heterogenous devices

Responsibilities

Contribute to original research in deep learning with a focus on modular architectures, verifiability, continual learning, and scale
Design and prototype novel neural network architectures for decentralized compute environments
Contribute to joint publications and projects in collaboration with academic and industry researchers targeting top-tier AI venues such as NeurIPS, ICML, and ICLR

Competencies

Must Have

Currently enrolled in a PhD program (or, in exceptional cases, in a Master’s program) in Computer Science, Machine Learning, or a related field
Prior experience conducting original research, ideally with authorship or co-authorship on ML papers
Strong understanding of deep learning fundamentals and experience working with in at least one major framework, e.g. PyTorch, JAX, or TensorFlow
Self-directed, curious, and able to thrive in an environment with high autonomy
Excellent written and verbal communication skills

Preferred

Research experience in distributed systems, continual learning, or modular neural architectures
A desire to contribute to open research and collaborate with the broader ML research community

Nice to Have

Experience at the intersection of cryptography and machine learning

Please note: the benefits listed below apply to full-time employees only

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