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Anyscale

Scales AI workloads with Ray platform

Staff Software Engineer - Platform Infrastructure, Foundations

Platform EngineeringConfirmed live in the last 24 hours
Full-timeSan Francisco, CA, USAHybrid

About the Job

Requirements

  • At least 5 years of experience writing high-quality production code and leading complex distributed systems projects.
  • A proven track record designing and maintaining highly available, scalable, and secure cloud-native platforms using AWS, Azure, or Google Cloud Platform.
  • Deep expertise in Kubernetes-based deployments and container orchestration at massive scale.
  • Advanced knowledge of the Linux kernel, networking, and low-level operating system foundations.
  • Mastery of Go and Python, with the ability to set coding standards and best practices for the team.
  • Ability to mentor senior engineers, influence technical direction without direct authority, and navigate complex trade-offs.

Responsibilities

  • Define and drive the multi-year technical roadmap for services that orchestrate Ray clusters across diverse cloud and on-premises environments.
  • Lead the design and optimization of high-performance control plane components tailored for large-scale, heterogeneous AI/ML workloads.
  • Establish organization-wide standards for the reliability, scalability, and observability of Anyscale-managed infrastructure.
  • Direct the long-term strategy for accelerator integration, including GPUs and TPUs, and container management to ensure seamless execution of distributed workloads.
  • Lead complex design and architecture discussions, resolve deep technical debt, and ensure engineering excellence across the organization.
  • Partner with ML experts and customer-facing teams to translate market needs into robust infrastructure foundations.

About the Company

Anyscale

Scales AI workloads with Ray platform

Anyscale helps enterprises run AI workloads at scale by providing a software platform built around the Ray open-source framework. The core product enables users to deploy, manage, and optimize distributed AI tasks—from training to inference—across large clusters, with features that handle scaling, fault tolerance, and resource management. The platform is delivered as a software-as-a-service, so customers pay a subscription to access tools for running Generative AI, large language models, computer vision, and other ML workloads efficiently and reliably. Unlike others who focus on individual components, Anyscale combines Ray’s distributed execution with enterprise-ready management, monitoring, and optimization to productionize AI applications. The company’s goal is to help organizations deploy AI workloads faster, at scale, with predictable performance and cost efficiency.

Company size
501-1,000
Company stage
Acquired
Total funding
$259.6M
Headquarters
San Francisco, CA
Founded
2019

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