Jobs at Anyscale

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 technical programs for Anyscale's core product, coordinating engineering and product teams to plan requirements, identify risks, manage schedules, and deliver commercial ML/AI solutions on cloud and big-data platforms.

Own and scale HR operations including onboarding/offboarding, HRIS administration, benefits, compliance, payroll coordination, and People data. Build processes, dashboards, and run programs like performance reviews and engagement surveys while partnering with Finance, Legal, and IT.

Build and optimize Ray Data: improve performance and scaling for multi-modal batch inference, implement production data-loading solutions, ensure stability and fault tolerance at high scale, and collaborate with customers to scale AI workloads.

Build and scale control and data plane infrastructure for Ray-based distributed AI workloads. Design orchestration, scheduling, resource management, observability, and accelerator integration for VM and Kubernetes deployments. Support on-call operations, performance and reliability improvements, and collaborate on open-source and proprietary integrations.

Bay Area-based hunter Enterprise Account Executive responsible for prospecting, developing, and closing new enterprise business for Anyscale. Manage complex sales cycles, achieve quotas, lead CxO engagements, qualify opportunities, collaborate with marketing, and drive ongoing account growth while advising customers on ML, compute, and cloud solutions.

The role involves developing open source software for distributed programming, optimizing performance, improving testing processes, and communicating findings publicly.

Lead Ray Data, a scalable data processing library, by managing the product roadmap, encouraging open source adoption, and engaging with customers.

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.

The Customer Engineer manages post-sale customer journeys, resolves issues, advises customers on Anyscale platform, and collaborates with engineering teams.

Partner with strategic customers to lead on-site proof-of-value engagements, deploy and drive enterprise adoption of Ray and the Anyscale platform, build demos and reference architectures, advise stakeholders, accelerate deals, and relay customer feedback to product and engineering.

The Software Engineer will develop and optimize infrastructure for distributed AI workloads, focusing on cloud-native deployments and systems orchestration while enhancing reliability, performance, and scalability.

The role focuses on optimizing ML inference at scale, collaborating with product teams, integrating Ray Data, and enhancing open-source contributions.

Develop high-quality open-source software to simplify distributed programming while working on architectural improvements and enhancing testing processes for the Ray core.

Develop high-quality software for distributed programming, optimize Ray's Datasets library, and improve testing processes. Communicate work through tutorials and talks.

Design and implement scalable cloud infrastructure for distributed AI applications, focusing on control and data planes, optimizing performance and reliability of services, and integrating with existing tools.