Machine Learning Engineer
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Requirements
• Strong background in applied machine learning and/or reinforcement learning, with hands-on experience training, evaluating, and optimizing models. • Proven experience building or scaling ML systems (pre-training, post-training, inference) • Comfortable working in an experimental environment, with extremely high autonomy and unpredictable timelines • Impeccable analytical and problem-solving skills • Strong software engineering fundamentals: data structures, algorithms, and system architecture. • Preferred • Experience building highly performant, distributed systems • Demonstrated experience operationalizing novel ML research, bridging experimentation and production • Familiarity with decentralized or heterogeneous compute environments and distributed orchestration at scale. • Experience developing mission-critical, fault-tolerant ML infrastructure • Experience working in a startup/scaleup environment • Previous experience working with smart contracts
Responsibilities
• Build scalable, distributed ML compute systems that operate efficiently over uniquely decentralised and heterogeneous infrastructure • Develop and optimize reinforcement learning and applied ML pipelines, improving performance, reliability, and reproducibility. • Collaborate with both researchers and production engineers to design and run novel experiments, taking research from theory to production. • Prototype and evaluate new ML architectures, tools, and frameworks to accelerate experimentation and deployment. • Competencies • Competencies
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 • 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.