Senior ML Platform Engineer - Remote-First, High-Impact

Coinbase, Inc.; Boise (ID); On-site; USD 191,000 - 225,000; Full time

Benefits offered by this job

Equity

Bonus eligibility

Medical insurance

Dental insurance

Vision insurance

401(k)

Job summary

Coinbase, Inc. is seeking a Senior Machine Learning Platform Engineer to build and maintain the core ML infrastructure that powers feature engineering, model training, and model serving across the company.

You will own systems spanning streaming, distributed training, and high-availability inference, shaping ML scale. The role emphasizes mentorship, quality, and responsible AI practices in a high-growth environment.

Qualifications

• 5+ years as a software engineer with distributed systems in production.
• Experience with low-latency data/ML infrastructure.
• Experience mentoring engineers and improving code quality.
• Familiarity with ML platform components (feature stores, model serving frameworks, training orchestration).
• Generative AI usage with human oversight to improve workflows.

Responsibilities

• Own design and reliability of ML inference infrastructure for both predictive models and LLMs.
• Build and optimize low-latency streaming pipelines delivering fresh feature data to production ML models.
• Drive improvements to distributed training infrastructure enabling efficient processing of large data volumes.
• Develop observability tooling to monitor data quality and detect degradations affecting model performance.
• Mentor junior engineers to raise engineering standards across the team.

Skills

Distributed systems

Low-latency infra

Mentoring

ML platform components

Generative AI governance

Tools

Feature stores

Model serving frameworks

Training orchestration

Job description

Coinbase, Inc. is seeking a Senior Machine Learning Platform Engineer to build and maintain the core ML infrastructure that powers feature engineering, model training, and model serving across the company.

You will own systems spanning streaming, distributed training, and high-availability inference, shaping ML scale. The role emphasizes mentorship, quality, and responsible AI practices in a high-growth environment.