Staff Machine Learning Engineer - Platform (Core Automation)

<div class="content-intro"><p>Ready to be pushed beyond what you think you’re capable of?</p> <p>At Coinbase, our mission is to increase economic freedom in the world. It’s a massive, ambitious opportunity that demands the best of us, every day, as we build the emerging onchain platform — and with it, the future global financial system.</p> <p>To achieve our mission, we’re seeking a very specific candidate. We want someone who is passionate about our mission and who believes in the power of crypto and blockchain technology to update the financial system. We want someone who is eager to leave their mark on the world, who relishes the pressure and privilege of working with high caliber colleagues, and who actively seeks feedback to keep leveling up. We want someone who will run towards, not away from, solving the company’s hardest problems.</p> <p>Our <a href="https://www.coinbase.com/mission">work culture</a> is intense and isn’t for everyone. But if you want to build the future alongside others who excel in their disciplines and expect the same from you, there’s no better place to be.</p> <p>While many roles at Coinbase are remote-first, we are not remote-only. In-person participation is required throughout the year. Team and company-wide offsites are held multiple times annually to foster collaboration, connection, and alignment. Attendance is expected and fully supported.</p></div><p>The Core Automation Team is a highly driven and innovative group dedicated to developing AI infrastructure and automation solutions that enhance productivity, foster innovation, and create meaningful customer impact across Coinbase. Our mission is to streamline operations for customer support and compliance teams while delivering exceptional customer experiences. As a Machine Learning Engineer on our team, you will play a key role in this transformation, utilizing state-of-the-art AI models, frameworks, and tools, along with diverse and extensive data sources, including blockchain data. Your work will focus on breaking down barriers to cryptocurrency adoption and shaping the future of AI-powered customer interactions.</p> <p><strong>What you’ll be doing (ie. job duties)</strong>:</p> <ul> <li>Explore and apply advanced machine learning techniques, including large language models (LLMs) and deep learning, to solve complex challenges across the organization.</li> <li>Collaborate with management and stakeholders to define strategic roadmaps and translate them into actionable quarterly plans.</li> <li>Drive project execution by managing priorities, deadlines, and deliverables, leveraging your technical expertise.</li> <li>Develop high-quality, well-tested Python code to address critical product and business needs.</li> <li>Design scalable, secure systems using state-of-the-art AI/ML technologies and industry best practices.</li> <li>Mentor team members on design principles, coding standards, and the adoption of AI productivity tools.</li> <li>Troubleshoot and resolve complex technical issues to improve system reliability, scalability, and operational efficiency.</li> <li>Ensure the security, scalability, and architectural integrity of feature designs through thorough reviews across teams.</li> </ul> <p><strong>What we look for in you (ie. job requirements)</strong>:</p> <ul> <li>8+ years of experience in machine learning and software engineering.</li> <li>Proven expertise in designing, building, scaling, and maintaining production services, with a strong focus on data processing/machine learning/analytics.</li> <li>Hands-on experience with at least one ML model type, such as LLMs, GNNs, Deep Learning, Logistic Regression, or Gradient Boosting Trees.</li> <li>Proficiency in one or more areas, including data mining, information retrieval, advanced statistics, natural language processing, or computer vision.</li> <li>Ability to write high-quality, well-tested code that effectively addresses customer needs.</li> <li>Experience mentoring and developing j ... (truncated, view full listing at source)