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Salary: USD 191,250 - 225,000 / annual
As a Senior Machine Learning Platform Engineer on the ML Platform team at Coinbase, you will build foundational infrastructure that powers feature engineering, model training, and model serving across the organization. The platform supports fraud detection, user personalization, and blockchain analysis, with critical systems spanning stream processing, distributed training, and highly available inference services.
Key responsibilities include:
- Owning the design and reliability of ML inference infrastructure serving both predictive models and LLMs, maintaining high availability and low latency at scale
- Building and optimizing low-latency streaming pipelines that deliver fresh, high-quality feature data to production ML models
- Driving improvements to distributed training infrastructure to enable ML engineers to process large data volumes efficiently
- Developing observability tooling to monitor data quality entering models and detect degradations impacting model performance
- Mentoring junior engineers on building production-grade software and raising the team's engineering standards over time
Coinbase is a remote-first company with quarterly in-person working sessions called "surges." The organization is mission-driven around increasing economic freedom and maintains a high-intensity, high-bar environment.
REQUIREMENTS:
- 5+ years of industry experience as a software engineer, with demonstrated ownership of distributed systems in production environments
- Built and operated low-latency data or ML infrastructure (streaming pipelines, online serving systems, or distributed training) processing data at scale
- Track record of mentoring engineers and raising team engineering quality through code reviews, design reviews, and technical leadership
- Familiarity with ML platform components (feature stores, model serving frameworks, training orchestration) sufficient to partner effectively with ML engineers as a platform builder
- Ability to utilize generative AI responsibly, maintaining human oversight to deliver business-ready outputs and drive measurable improvements in workflow efficiency, cost, and quality
