Senior ML Platform Engineer: Scalable Inference & Streaming

Coinbase, Inc.; Sacramento (CA); Hybrid; USD 191,000 - 225,000; Full time

Job summary

Coinbase, Inc. is seeking a Senior Machine Learning Platform Engineer to build scalable infrastructure powering feature engineering, model training and serving across the company.

You'll own ML inference systems, streaming pipelines, distributed training, and observability tooling, mentoring engineers and collaborating with ML teams to raise production quality while maintaining strong human oversight of AI workloads.

Qualifications

• 5+ years of industry experience as a software engineer, with ownership of distributed systems in production.
• Built and operated low-latency data or ML infrastructure processing data at scale.
• Mentored engineers and raised team engineering quality through code reviews, design reviews, and technical leadership.
• Familiarity with ML platform components (feature stores, model serving frameworks, training orchestration).
• Uses generative AI responsibly with human oversight to deliver business-ready outputs.

Responsibilities

• 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 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 performance.
• Mentor junior engineers on building production-grade software and raise engineering standards over time.

Skills

Distributed systems

Low-latency infra

Mentoring

ML platforms

Generative AI governance

Job description

Coinbase, Inc. is seeking a Senior Machine Learning Platform Engineer to build scalable infrastructure powering feature engineering, model training and serving across the company.

You'll own ML inference systems, streaming pipelines, distributed training, and observability tooling, mentoring engineers and collaborating with ML teams to raise production quality while maintaining strong human oversight of AI workloads.