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The Enterprise Account Executive will prospect, develop, and close enterprise software and cloud sales opportunities in the Bay Area. Responsibilities include building account strategies, generating pipeline, managing complex sales cycles, conducting executive meetings, negotiating agreements, achieving quotas, and expanding customer revenue. The role requires technical fluency in machine learning, cloud, and infrastructure solutions, along with strong presentation, communication, and relationship-management skills.

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.

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.