Senior Machine Learning Platform Engineer

Location: Remote - USA

Department: Machine Learning

Senior Machine Learning Platform Engineer

As a Senior Machine Learning Platform Engineer on the ML Platform team within the Platform group, youll build the foundational infrastructure that powers feature engineering, model training, and model serving across Coinbase. The platform you build supports fraud detection, user personalization, and blockchain analysis. Youll own critical systems spanning stream processing, distributed training, and highly available inference services, directly shaping how ML scales across the company.

What youll do:

• Own the design and reliability of ML inference infrastructure serving both predictive models and LLMs, maintaining high availability and low latency at scale.
• Build and optimize low-latency streaming pipelines that deliver fresh, high-quality feature data to production ML models.
• Drive improvements to distributed training infrastructure, enabling ML engineers to process large data volumes efficiently.
• Develop observability tooling to monitor data quality entering models and detect degradations impacting model performance.
• Mentor junior engineers on building production-grade software, raising the teams engineering standards over time.

Required Skills and Experience:

• 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.
• Utilizes generative AI responsibly, maintaining human oversight to deliver business-ready outputs and drive measurable improvements in workflow efficiency, cost, and quality.