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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Machine Learning Platform Engineer (Platform) - **Company:** Coinbase, Inc. - **Location:** Charlotte, NC, 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 29, 2026 - **Apply:** https://dejobs.org/x/x/CA5A1F9D28EC403CAE01DE568A28CCDE/job/ ## 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., Depending on your location, the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA) may regulate the way we manage the data of job applicants. Our full notice outlining how data will be processed as part of the application procedure for applicable locations is available here. By submitting your application, you are agreeing to our use and processing of your data as required. For US applicants only, by submitting your application you are agreeing to arbitration of disputes as outlined here. ## Related Videos - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Go with the Flow: Stop the Leaks Before Your Memory's a Waterfall!](https://www.wearedevelopers.com/videos/100073-go-with-the-flow-stop-the-leaks-before-your-memory-s-a-waterfall) - [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)