Anyscale

Anyscale

Customer Engineer, Infrastructure

San Francisco · Senior

Sponsorship not specifiedDetected 1 day ago
Distributed SystemsGitAWSGCPAzureCloud PlatformsKubernetesTerraformCI/CDGitHub ActionsMachine LearningData EngineeringData ScienceLLMsMLOpsCustomer SupportCommunicationMentoringOrganizational Skills

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odds of building a lasting career here

38Risky
Cap-exempt (no lottery)0
Sponsors this role82
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70

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Files H-1B transfers

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About the role

  • The Customer Engineer will play a crucial role in the customers' post-sale journey - helping them to onboard, adopt and grow on Anyscale, troubleshooting and resolving open customer tickets and driving consumption.
  • Anyscale is an ever evolving platform and hence will require close co-ordination with our engineering teams to debug complex issues.
  • This is an exciting role for those who are technically curious and passionate about ML/AI, LLM, vLLM and the role of AI in next generation applications.

Responsibilities

  • Own customer issues end-to-end, from troubleshooting, triaging, escalations and eventual resolution
  • Participate in our follow-the-sun customer support model to ensure continuity in resolving high priority tickets
  • Habitually provide feedback and collaborate cross-functionally with product and engineering teams to address customer issues with a focus on improving the product experience
  • Build and maintain strong relationships with technical stakeholders within customer accounts

Requirements

  • Experience with Ray
  • Knowledge of MLOps platforms
  • Knowledge of container orchestration platforms (e.g., Kubernetes), infrastructure as code (e.g. Terraform), CI/CD tools (e.g. Github Actions)
  • 7+ years of experience in a Machine Learning role in a dynamic, fast-paced, startup-like environment
  • Strong organizational skills and ability to manage multiple customer needs simultaneously
  • Proficient at developing data pipelines for training, fine-tuning and inference/serving of LLMs
  • We're on a mission to make scalable computing effortless. Ray is the AI Compute Engine at the center of some of the world's most powerful AI platforms
  • Our tech is in production at companies like OpenAI, Uber, Spotify, Instacart, and Cruise
  • We're backed by Andreessen Horowitz, NEA, and Addition, with $250M+ raised to date

Skills

  • About Anyscale
  • Proud to be backed by Andreessen Horowitz, NEA, and Addition with $250+ million raised to date.
  • Contribute towards improvement of internal tools and documentation of playbooks, guides and best practices etc. based on observed patterns
  • Experience running and optimizing infrastructure for distributed ML workloads on the major cloud platforms (AWS/EKS, GCP/GKE or Azure/AKS)
  • Excellent communication and interpersonal skills.
  • Strong sense of ownership, self-motivation and eagerness to acquire new skills and do new things
  • Willingness to up-level the knowledge and skills of your peers through mentorship, trainings and shadowing
  • Recent partnerships with Azure, CoreWeave, and Google Cloud are putting AI-native compute directly into enterprise environments

Compensation

  • Competitive salary and equity, plus health/dental/vision coverage (many plans up to 99% employer-covered)

Benefits

  • Competitive salary and equity, plus health/dental/vision coverage (many plans up to 99% employer-covered)
  • We offer flexible time off, paid parental leave, and mental health support

Company info

  • About Anyscale At Anyscale, we're on a mission to democratize distributed computing and make it accessible to software developers of all skill levels.
  • About the role The Customer Engineer will play a crucial role in the customers' post-sale journey - helping them to onboard, adopt and grow on Anyscale, troubleshooting and resolving open customer tickets and driving consumption.
  • Be a technical advisor, and internal champion for our key customers
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This listing is sourced directly from Anyscale's careers page and normalized into a canonical job model.