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Design, build, optimize, and scale Ray Data’s distributed data processing infrastructure for large-scale AI training and inference. Responsibilities include distributed execution, scheduling, resource management, data partitioning, fault tolerance, performance optimization, and system architecture. The role also involves collaborating with customers and AI-native companies to solve complex workload-scaling challenges.

Develop and maintain Ray’s C++ backend for scalable, reliable distributed computing. Responsibilities include optimizing large-scale workloads, improving fault tolerance, developing open-source software, implementing architectural improvements, enhancing testing infrastructure, leading cross-team projects, mentoring engineers, and communicating technical work through talks, tutorials, and blog posts.

Own Anyscale’s compliance function, including SOC 2 and ISO 27001 audits, evidence collection, risk management, customer security diligence, contractual compliance obligations, and future certifications. Partner with security, engineering, IT, legal, sales, and external auditors to maintain audit-ready controls, improve evidence automation, and establish an enterprise-aligned compliance program.

Own Anyscale’s IT engineering function across identity and access, endpoint management, hardware logistics, SaaS vendors, audit controls, and office technology. Build and operate internal automation and notification services, manage Okta and Google Workspace, administer the macOS fleet, automate HRIS-driven provisioning, coordinate hardware procurement and shipping, manage vendor renewals, and support audit evidence collection. This hybrid San Francisco Bay Area role emphasizes secure automation, code ownership, and end-to-end accountability.

Build developer tooling, MLOps capabilities, CLIs, SDKs, APIs, observability features, and highly available services for distributed AI workloads on Ray. Own projects end to end across data preparation, model fine-tuning, evaluation, serving, deployment, debugging, and optimization. Collaborate with users, field teams, and machine learning and distributed-systems experts to deliver intuitive, scalable developer experiences.

Own detection engineering and incident response across corporate and production environments. Build cloud, endpoint, container, runtime, correlation, and alerting capabilities; lead live incidents; develop and exercise response runbooks; tune detections; measure detection and response performance; conduct retrospectives; and partner with infrastructure security and IT to address identified gaps.

Design, build, and scale control- and data-plane infrastructure for distributed AI workloads. Improve reliability, performance, scheduling, and observability for Ray clusters across cloud and on-prem environments. Support accelerator integration, container image management, and provide on-call troubleshooting and cross-team collaboration.

Lead and scale a Customer Engineering team supporting production AI workloads. Own operational health, customer escalations, and technical issue resolution. Partner with Product and Engineering to translate customer feedback into product improvements. Drive automation, diagnostics, documentation, and tooling to reduce recurring issues and improve customer outcomes.

Lead Infrastructure, SRE, and Enterprise Governance Engineering teams to design and operate Anyscale's platform. Own strategy and delivery for cluster launcher, cloud providers (AWS/GCP/Azure), Kubernetes support, autoscaling, control and data planes, reliability, billing, and production databases. Recruit, mentor, and manage a high-performing team while partnering with customers and field engineering to solve distributed systems challenges.

Develop and optimize Ray Data, a Python-native distributed data processing engine for AI workloads. Improve performance, scalability, data loading, stability, and fault tolerance across heterogeneous environments. Build production training data solutions and collaborate with customers and AI-native companies to scale large machine learning workloads.