Machine Learning Engineer

\=About EQUALS EQUALS is the music social network. We have 1 million monthly active users, people use us more than Instagram, Snapchat, and X, and we have sparked over 40 million friendships. We are a team of creatives and technologists from giants like Nike, Instagram, and Apple, backed by the investors behind Spotify, A24, and Facebook, making us one of the most well funded consumer social businesses in the game. Our mission is to unite the world through its most universal language: music. \=Your Role As a Machine Learning Engineer at EQUALS, you will independently own the feed recommendations, the safety systems, and the people recommendation surfaces that sit at the heart of the product. You will own these problems end to end, from data and modelling through to serving, evaluation, and iteration in production. With 1M monthly actives and engagement that already beats the incumbents, the signal is rich and the stakes are real. We are looking for someone with deep technical range, a bias for shipping, and taste for the kind of experience that keeps people coming back. \=Your Realm Feed Recommendations - Own the content recommendation systems that decide what music and content every user sees. - Design, train, and ship ranking and retrieval models end to end, from candidate generation to final ranking. - Build the evaluation and experimentation loops (offline metrics, online A/B tests) that let us move fast without flying blind. - Balance engagement, discovery, and artist growth so the feed serves users, superfans, and musicians alike. People Recommendation - Build the systems that connect people to the right artists, communities, and each other, and turn 40 million friendships into many more. - Model social graphs, affinity, and taste to power follow, friend, and community suggestions. - Turn cold start and new user onboarding into a first class experience rather than an afterthought. Trust \& Safety - Own the ML that keeps the platform safe, from content classification and abuse detection to moderation tooling. - Build systems that scale as we grow past our first million users and adapt as new failure modes emerge. - Partner across teams to make safety a native part of the product, not a bolt on. Platform \& Foundations - Stand up the data pipelines, feature infrastructure, and serving systems these models depend on. - Instrument everything. Collaborate with analysts to derive insight and optimise performance. - Make pragmatic build versus buy calls and keep the stack lean enough to move quickly. \=Who You Are - A strong ML engineer who has shipped recommendation, ranking, or search systems to real users at scale. - Comfortable owning a problem end to end, from ambiguous brief to production system to measured impact. - Fluent in Python and the modern ML stack, and unafraid to go deep into infrastructure when the problem demands it. - Rigorous about evaluation. You know that a good offline number is a hypothesis, not a result. - A pragmatist who ships, iterates, and improves rather than polishing in a vacuum. - Genuinely curious about consumer social products and how taste, culture, and behaviour show up in data. - A team player, adept at cross functional collaboration, and an effective communicator across all levels. - Bonus: a real love of emerging electronic music and internet culture. \=Our Values (1) Brand - Our brand is everything, and everything is our brand. - We prioritise quality in all we ship. - We do shit worth talking about. (2) Innovation - We don't believe in ceilings. - Shit that needs to be done gets done. - With change comes opportunity. We embrace change. (3) Commitment - See a problem, fix the problem. - Commitments are kept. - We negotiate to provide the best outcome for ourselves and our users.