Senior Machine Learning Engineer, Platform

Block•Bay Area, CA, United States of America, CA

1d•$194,500 - $343,100•Remote

About The Position

Block’s Machine Learning Platform team builds shared infrastructure that helps teams develop, evaluate, deploy, and monitor models and automated decision systems for applications such as fraud detection, recommendations, and underwriting. The Senior Machine Learning Engineer will work closely with applied machine learning teams across Block to shape the roadmap, improve existing services, and build new platform capabilities. The role offers opportunities to deepen expertise across the ML lifecycle, influence system designs, and support other engineers through mentoring and design reviews. The position focuses on designing, building, and operating shared ML infrastructure, including feature stores, rules engines, model hosting and inference, training infrastructure, and offline model evaluation. It also involves leading large-scale distributed systems work, gathering feedback from internal platform users, and building reliable, self-service tools and services. The engineer participates in on-call rotations and incident response for mission-critical systems.

Requirements

8+ years of experience in software development and/or machine learning, 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 and training through model evaluation, MLOps, and productionizing classical ML, deep learning, and LLM models.
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 on-call work, incident response, and reliability improvements.
Experience leading complex technical work from an unclear starting point through delivery, including defining problems, testing ideas, choosing designs, planning work with other teams, and checking results.
Experience working with teams that use your platform, explaining technical choices and trade-offs, and mentoring engineers.
Experience using a major cloud vendor for high-scale production use cases.
Strong verbal and written communication skills with technical and non-technical stakeholders.
Ruthless pragmatism when solving new and unconventional problems.

Responsibilities

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 challenges, identify common needs, and turn them into reusable platform capabilities; use feedback to help set the roadmap and priorities.
Build reliable services through automated testing, monitoring, capacity planning, and safe deployments.
Participate in the on-call rotation for mission-critical systems, lead incident response and reviews, and address 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 their performance.
Mentor engineers, guide design reviews, and share practices that help the team build and maintain reliable systems.