• 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.