Senior Machine Learning Engineer, Platform

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Block is one company built from many blocks, all united by the same purpose: economic empowerment. We build technology that increases access and independence for sellers, individuals, and artists around the world. Working at Block means working on and across our brands—Square, Cash App, Afterpay, TIDAL, Spiral, and Proto— in roles that connect, enable, and push all of them forward faster. We’re remote-first by design and operate with intelligence at the center of everything we do. At Block, you'll be challenged by some of the brightest, most creative minds in the industry and have the tools and autonomy to do the most meaningful work of your career. The Role Block’s Machine Learning (ML) Platform team builds shared infrastructure that helps teams develop, evaluate, deploy, and monitor models and automated decision systems for uses such as fraud detection, recommendations, and underwriting. You will work closely with applied machine learning teams across Block to shape our roadmap, improve established services, and build new platform capabilities. You will have opportunities to deepen your expertise across the ML lifecycle, influence system designs, and help other engineers grow through mentoring and design reviews. You Will Design, build, and operate shared ML infrastructure, including feature stores, rules engines, model hosting and inference, training infrastructure, and offline model evaluation. Lead the design and delivery of large-scale distributed systems, balancing response time, throughput, data consistency, availability, and cost. Work with applied machine learning teams and internal customers to understand their challenges, identify common needs, and turn them into reusable platform capabilities; use that feedback to help set our roadmap and priorities. Build reliable services through automated testing, monitoring, capacity planning and safe deployments. Participate in the oncall rotation for mission-critical systems, lead incident response and reviews, and fix underlying causes to prevent repeat incidents and reduce manual support work. Build self-service interfaces, libraries, tools, and documentation that help teams develop models, move them into production, and understand how they perform. Mentor engineers, guide design reviews, and share practices that help the team build and maintain reliable systems. You Have 8+ years of experience in software development and/or machine learning experience with a focus on internal platforms or infrastructure. Hands-on experience building and operating ML infrastructure, with depth in one or more areas such as feature stores, rules engines, model hosting and inference, or training infrastructure. Deep understanding of architectures for ML-backed solutions and the ML lifecycle - from data gathering, training, model evaluation, MLOps, and productionizing models (both classical ML and deep learning/LLMs). Experience designing and operating large-scale distributed systems, including understanding failure modes, maintaining correct data, and managing performance and capacity. Experience operating mission-critical services, including oncall, incident response, and improvements that made systems more reliable. Experience leading complex technical work from an unclear starting point through delivery: defining the problem, testing ideas, choosing a design, planning work with other teams, and checking the results. Experience working with teams that use your platform, explaining technical choices and trade-offs, and mentoring engineers. Experience using any of the major cloud vendors for high scale production use cases. Strong communication skills (verbal and written) with technical and non-technical stakeholders. Ruthless pragmatism when solving new and unconventional problems Technologies We Use and Teach Kotlin / Java, Golang, Python, Typescript, React gRPC, Protocol Buffers AWS (Sagemaker, EC2, EKS, Dynamo), Databricks (Spark) NVidia Triton, vLLM, ONNX W

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