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# Senior Machine Learning Platform Engineer (Platform)

- **Company:** Coinbase, Inc.
- **Location:** Lansing, MI, United States
- **Experience:** Expert
- **Salary:** $186,065.0 - $225,000.0
- **Contract:** Permanent contract
- **Skills:** Clean Code Principles, Airflow, Big Data, Programming Tools, Distributed Systems, Amazon DynamoDB, Python (Programming Language), Machine Learning, Large Language Models, Snowflake, Apache Spark, Low Latency, Machine Learning Operations, Databricks, Golang
- **Published:** July 30, 2026
- **Apply:** https://www.techcareers.com/job.asp?id=3336270231&tx=KP505PFK&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

* 5+ yrs of industry experience as a Software Engineer.
 * You have a strong understanding of distributed systems.
 * You lead by example through high quality code and excellent communication skills.
 * You have a great sense of design, and can bring clarity to complex technical requirements.
 * You treat other engineers as a customer, and have an obsessive focus on delivering them a seamless experience.
 * You have a mastery of the fundamentals, such that you can quickly jump between many varied technologies and still operate at a high level.
 * Demonstrates the ability to responsibly use generative AI tools and copilots (e.g., LibreChat, Gemini, Glean) in daily workflows, continuously learn as tools evolve, and apply human-in-the-loop practices to deliver business-ready outputs and drive measurable improvements in efficiency, cost, and quality.

Nice to haves:

 * Experience building ML models and working with ML systems.
 * Experience working on a platform team, and building developer tooling.
 * Experience with the technologies we use (Python, Golang, Ray, Tecton, Spark, Airflow, Databricks, Snowflake, and DynamoDB).

## Description

* Form a deep understanding of our Machine Learning Engineers' needs and our current capabilities and gaps.
 * Mentor our talented junior engineers on how to build high quality software, and take their skills to the next level.
 * Continually raise our engineering standards to maintain high-availability and low-latency for our ML inference infrastructure that runs both predictive ML models and LLMs.
 * Optimize low latency streaming pipelines to give our ML models the freshest and highest quality data.
 * Evangelize state-of-the-art practices on building high-performance distributed training jobs that process large volumes of data.
 * Build tooling to observe the quality of data going into our models and to detect degradations impacting model performance.


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