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Machine Learning Engineer
Machine Learning Engineer at EQUALS
About EQUALS
EQUALS is the music social network. We have 1 million monthly active users, surpassing platforms like Instagram, Snapchat, and X, and have sparked over 40 million friendships.
We are a team of creatives and technologists from industry giants like Nike, Instagram, and Apple, backed by investors behind Spotify, A24, and Facebook. As one of the most well-funded consumer social businesses, 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 critical systems:
- Feed recommendations (the core experience)
- Safety systems (platform integrity)
- People recommendation surfaces (friendships and connections)
These systems power 1M monthly actives and drive engagement that outperforms incumbents. We are seeking an engineer with:
- Deep technical expertise
- A bias for shipping
- Strong taste in user experience
Your Realm
Feed Recommendations
- Architect, design, and scale ranking/retrieval models end-to-end (candidate generation → final ranking).
- Build evaluation loops (offline metrics + online A/B testing) to ensure data-driven, empirical progress.
- Balance trade-offs between engagement, discovery, and artist growth to serve:
- Users ( ocasiónipers Signal
- Superfans (deeper connections)
- Musicians (growth and visibility)
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People Recommendation
- Scale systems connecting users with artists, communities, and other users.
- Model social graphs, affinity, and taste synergies for better follow/friend/community suggestions.
- Redefine cold-start challenges in onboarding, ensuring all users thrive from day one.
Trust & Safety
- Own ML-driven safety protocols, including:
- Content classification
- Abuse detection
- Moderation tooling
- Ensure systems scale with growth beyond 1M users while adapting to unknown failure modes.
- Partner cross-functionally to make safety a core feature, not an afterthought.
Platform & Foundations
- Build data pipelines, feature robust : (love, not misses)frames, and serving systems required for ML models.
- Instrument every metric to enable data-driven iteration and optimization.
- Make pragmatic build-buy decisions to maintain stack leanness and speed.
Who You Are
Technical Expertise
- Shipped recommendation, ranking, or search systems to real users at scale before.
- Comfortable with end-to-end problem ownership (from vague brief to production impact measurement).
- Fluent in Python and the modern ML stack (TF/PyTorch, Spark, etc.).
- Willing to dive deep into infrastructure when problems demand it.
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Mental Model
- Rigorous about evaluation: offline metrics are hypotheses, not proof — always test.
- Pragmatic shipper: iterate without perfectionism, shipping, learning, improving repeatedly.
- Consumer social product knowledge: curious how taste/behavior/culture shapes data.
Collaborative Mindset
- Strong team player and cross-functional collaborator.
- Skilled at effective communication with all organizational levels.
Bonus
- A passion for emerging electronic music and internet culture.
Our Values
(1) Brand
- "Our brand is everything, and everything is our brand."
- Prioritize quality—no "good enough" here.
- Ships experiences worth talking about.
(2) Innovation
- "We don’t believe in ceilings."
- Execute whatever critical work needs doing, fast.
- Change is opportunity. Embrace transformation.
(3) Commitment
- "See a problem, fix the problem."
- Honor commitments—expedite transparency.
- Negotiate for balanced outcomes, for team impact and user value alike.
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
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