Base Jobs

We believe that the onchain platform is the most important builder platform since the internet (“online”). We believe that the onchain platform should be open source, free to use, and globally available. And we believe that in order to make it really work, we need all hands on deck, working together to scale in a secure, decentralized, easy-to-use way.

At Base, we live by our Stay Based values: we do the right thing, work hard, stay optimistic, prioritize the team, and push creative boundaries—all while building the future of onchain. We value a Zone 3+ work ethic, where our team rises to the challenge, embraces hard weeks, and makes small to significant personal tradeoffs when necessary to drive impact and innovation.

Base is incubated within Coinbase and plans to progressively decentralize in the years ahead. We believe that decentralization is critical to creating an open, global crypto economy that is accessible to everyone.

Base is built on Optimism’s open-source OP Stack. We’re joining as the second Core Dev team working on the OP Stack to ensure it’s a public good available to everyone; and contributing a portion of sequencer revenue to funding public goods.

The Base App Social Feed team is looking for a Machine Learning Engineer to help with building the foundation for a high-signal social and trading feed experience, focusing on relevance, discovery, safety, and performance. As an Machine Learning Engineer, you’ll design production-scale ranking and AI systems end-to-end, owning data pipelines, features and embeddings, retrieval and RAG, and online inference while partnering closely with Data Science to measure the impact of new ranking models.

What you'll be doing:

• Design, build, and iterate AI and ML-powered ranking and relevance systems (including candidate generation, filtering, personalization, bandits, and learning-to-rank) that balance engagement, quality, and safety while meeting latency and reliability SLOs.
• Develop features and embeddings for users, content, graph, temporal, and safety signals; establish feature contracts, offline backfills, and online freshness.
• Stand up vector retrieval and RAG pipelines for semantic search, content understanding, and safety heuristics; tune retrieval quality, token/latency budgets, and evaluation.
• Build and operate online inference and batch scoring services for LLMs and ML models; optimize throughput, cost, caching, and autoscaling; introduce GPU-aware scheduling where appropriate.
• Partner with Data Platform on data movement and pipelines (batch and streaming) that power ranking models and analytics; ensure data quality, lineage, documentation, and observability.
• Partner with Data Science to frame metrics and hypotheses, design trustworthy experiments, analyze results, and productionize improvements.
• Productionize models end-to-end (training, evaluation, versioning, registries, rollouts, shadowing/canary, rollback) with strong CI/CD and infra-as-code practices.
• Establish ML observability (data/feature drift, model performance, safety thresholds, online/offline eval, dashboards and alerting) to maintain quality over time.
• Collaborate cross-functionally with product, design, platform/infra, safety, and CX to align on goals, roadmap, and risk management.
• Contribute to engineering excellence through documentation, code reviews, reliability improvements, and mentorship to junior engineers.

What we look for in you:

• 3+ years of ML engineering or recommender-systems experience (or equivalent), including shipping production models and improving them iteratively.
• Strong software engineering fundamentals with proficiency in Python and SQL; experience building reliable services and data pipelines at scale.
• LLM and AI inference experience, including prompt/program design, evaluation frameworks, agentic workflows, or online performance optimization.
• Data pipelines and orchestration (batch and streaming), data quality/observability, and schema/feature contract management.
• ML operations (model registries, feature stores, experiment frameworks, offline/online evaluation, rollouts, and monitoring).
• Experimentation mindset with practical A/B testing design, metric selection, and rigorous analysis in partnership with Data Science.
• Clear, pragmatic communication and the ability to collaborate across product, design, and engineering to drive outcomes.
• A hunger to learn more about AI and ranking at production scale
• BS/MS in Computer Science, Machine Learning, or equivalent practical experience.

Nice to haves:

• Experience with social feeds, marketplace dynamics, or personalization at scale.
• Hands-on experience with ranking/relevance, candidate generation/two-tower models, embeddings/vector search, contextual bandits, or RAG or retrieval-enhanced workflows.
• Experience with content safety and quality signals (e.g., NSFW/abuse detection, watermarking, spam/fraud patterns) and ranking-time mitigation.
• Knowledge of privacy, compliance, and responsible AI considerations in production ML.
• Exposure to Go or other strongly typed languages used in high-performance backends.

Position ID: GBML04US

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Please be advised that each candidate may submit a maximum of four applications within any 30-day period. We encourage you to carefully evaluate how your skills and interests align with Coinbase's roles before applying.

Commitment to Equal Opportunity

Coinbase is proud to be an Equal Opportunity Employer.  All qualified applicants will receive consideration for employment without regard to race, color, religion, creed, gender, national origin, age, disability, veteran status, sex, gender expression or identity, sexual orientation or any other basis protected by applicable law. Coinbase will also consider for employment qualified applicants with criminal histories in a manner consistent with applicable federal, state and local law.  For US applicants, you may view the Employee Rights and the Know Your Rights notices by clicking on their corresponding links. Additionally, Coinbase participates in the E-Verify program in certain locations, as required by law. 

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AI Disclosure

For select roles, Coinbase is piloting an AI tool based on machine learning technologies to conduct initial screening interviews to qualified applicants. The tool simulates realistic interview scenarios and engages in dynamic conversation. A human recruiter will review your interview responses, provided in the form of a voice recording and/or transcript, to assess them against the qualifications and characteristics outlined in the job description.  

For select roles, Coinbase is also piloting an AI interview intelligence platform to transcribe and summarize interview notes, allowing our interviewers to fully focus on you as the candidate. 

The above pilots are for testing purposes and Coinbase will not use AI to make decisions impacting employment. To request a reasonable accommodation due to disability, please contact accommodations[at]coinbase.com.