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R&D, Graphs & Networks

Recurrent Labs / Currents.fm is a protocol and a platform – collectively, Currents.
We're a team of Artists from almost every continent who are building infrastructure for progressively decentralized, Artist-owned culture networks as a distribution layer on the blockchain.
We work to directly empower our worldwide grassroots scenes across over 100 countries and are building a new, open fabric redefining the future of the music economy.
We’re backed by an incredible group consisting of both founders and artists who believe in the vision of a decentralized, vibrant, and community-owned future, including Blockchain Capital, Protocol Labs, Anatoly Yakovenko (founder of Solana), and Backstage Capital.

Job Description

Recurrent Labs is building a world-class founding team. As one of our first engineers, you’ll help drive our technical decisions and influence our product direction for years to come, with the goal of redefining the future of the music economy.
This will involve R&D in graph and network models, building models of graph dynamics, developing protocol parameters, and identifying novel dynamics with the purpose of cultivating long-term strength in community trust networks.
You’ll report directly to our founder, Austin.
Characteristics of this role:
  • Full time
  • Can be fully remote
  • Competitive compensation & benefits
Your responsibilities will include:
  • Lead the development of network-based protocols for novel economic mechanisms that build trust across cultural communities
  • Identify and analyze network patterns of culture and value exchange across worldwide communities
  • Build models and create future predictions, analyze network structure, and identify regional differences in communities
  • Develop and deploy automated pipelines to support prototyping and proof of concepts across the team
  • Produce visualizations of graph and subgraph networks that show structural patterns
  • In the process, you’ll throw a lot of festivals and develop an up-to-the-second understanding for the future of music.

Traits we look for

Explorers / Thriving in Ambiguity
You have the ability to quickly assess large, amorphous problems and learn any tools needed to distill structure from chaos. You understand that it's never idealism vs. reality, but both. You are able to not just survive, but have a history of thriving when dropped in a foreign environment.
Artists / Music is Connection
You have a deep love of music, ****of discovering music, and of connecting with the people who celebrate it. You're constantly pushing forward in the pursuit of music, new and old, to listen to and share.
Builders / Action Before Motivation
You're a self-starter that loves action-focused environments. You're not afraid to make mistakes and have a strong bias towards iterating based on first-principles thinking. You're able to validate assumptions, manage expectations, and under-promise + over-deliver.
Perspectives / Creativity in Diversity
You are excited about how our differences enable us to solve complex, interdisciplinary problems. You build communities naturally and don't hesitate to hold space for others with a personal practice of inclusion, mindfulness, and most importantly, kindness.
Tinkerers / Constantly Learning
You like to get involved in things. You are always throwing yourself into projects. Whether they're creative pursuits, side hustles, or hobbies, you're constantly learning about the world through the act of creation.
Humanists / Tech is built for People
You see in systems. You understand that just like content, tech doesn't exist in a vacuum – it's a tool and its human impact is derived from the people behind it. You have a track record of creating impact while integrating nuanced context across culture, history, and industry.

Requirements

  • ~3-5 years of relevant experience (or a PhD) in mathematics, networks, graphs, statistics, or socioeconomic fields
  • Strong understanding of graph analytics programming models and runtimes
  • Familiarity with graph analytics problems at scale and knowledge of statistical patterns + complex networks
  • Strong skills in C/C++, Python, R, Neo4j, or equivalent languages