Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

Design and operate production AI/ML platforms for enterprise customers using Kubernetes, public cloud, Ray, and infrastructure-as-code. Lead customer implementations spanning architecture, deployment, networking, security, observability, scaling, upgrades, and operational readiness. Troubleshoot distributed systems, build automation and reusable tooling, advise technical and executive stakeholders, and collaborate with Product and Engineering to improve the platform.

Leads the open-source Ray Data engineering team, setting technical direction, managing the roadmap, driving project execution, and hiring and mentoring engineers. The role remains hands-on with design and code reviews, performance and stability investigations, production escalations, and distributed systems development. It also involves open-source community leadership, cross-team collaboration, customer engagement, release quality, and communication through technical content and events.

Lead the technical vision and architecture for Anyscale’s platform infrastructure, including control and data planes that orchestrate Ray clusters across cloud and on-premises environments. Design scalable distributed systems, establish reliability and observability standards, guide Kubernetes and container infrastructure, integrate GPUs and TPUs, resolve technical debt, mentor engineers, and influence cross-functional technical strategy.

Own the roadmap and technical direction for Ray Core, leading complex distributed-systems projects across multiple teams. Design and implement primitives involving scheduling, resource management, fault tolerance, concurrency, networking, storage, and performance. Remain hands-on in systems-oriented development and debugging while mentoring engineers, raising technical standards, and guiding Ray Core’s long-term architecture and production evolution.

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.

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.

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.