Forward Deployed Engineer - AI/ML Platforms

Speedrun Talent Network; San Francisco (CA); Presencial; USD 190.000 - 270.000; Jornada completa

Descripción de la vacante

Anyscale in San Francisco seeks a Forward Deployed Engineer - AI/ML Platforms to design, deploy, and operate production AI infrastructure for top customers using Ray. You’ll work with customer platform, infrastructure, and ML teams to build scalable AI platforms on Kubernetes and major clouds, and to operationalize distributed AI applications.

You’ll lead engagements from architecture to production readiness, develop automation, and translate customer needs into robust platform capabilities,

Formación

• 5+ years of experience in cloud infrastructure, platform engineering, DevOps, or software engineering.
• Experience building ML/AI platforms or large-scale data processing.
• Strong knowledge of Kubernetes and containerized production environments.

Responsabilidades

• Design and implement production-grade AI platform architectures on Kubernetes and public cloud infrastructure.
• Deploy, operate, and optimize distributed AI workloads for customers.
• Lead implementation engagements covering platform installation, networking, security, observability, scaling, upgrades.
• Troubleshoot complex distributed systems issues spanning infrastructure, Kubernetes, networking, storage, and AI applications.
• Develop automation and infrastructure-as-code to accelerate customer success.
• Build trusted relationships with technical leaders, platform teams, and executive stakeholders.
• Collaborate with Product and Engineering to communicate customer requirements and shape platform capabilities.
• Share best practices through technical documentation, architecture guidance, workshops, and enablement.

Conocimientos

Kubernetes

Cloud infra

DevOps

Python

Go

Java

Distributed systems

CI/CD

Terraform

GitOps

Networking

Herramientas

Terraform

Helm

CI/CD pipelines

Descripción del empleo

At Anyscale, we're on a mission to democratize distributed computing and make it accessible to software developers of all skill levels. We're commercializing Ray, a popular open-source project that's creating an ecosystem of libraries for scalable machine learning. Companies like OpenAI, Uber, Spotify, Instacart, Cruise, and many more, have Ray in their tech stacks to accelerate the progress of AI applications out into the real world.

With Anyscale, we're building the best place to run Ray, so that any developer or data scientist can scale an ML application from their laptop to the cluster without needing to be a distributed systems expert.

Proud to be backed by Andreessen Horowitz, NEA, and Addition with $250+ million raised to date.

About the role:

As a Forward Deployed Engineer - AI/ML Platforms at Anyscale, you'll partner with some of the world's most sophisticated AI organizations to design, deploy, and operate the infrastructure powering their production AI workloads.

In this role you will work directly with customer platform, infrastructure, and ML engineering teams to solve complex technical challenges. You will help customers build scalable AI platforms, modernize ML infrastructure, and operationalize distributed AI applications on Ray and the Anyscale platform.

You will combine deep cloud infrastructure expertise with strong customer engagement skills, serving as both a trusted technical advisor and a hands-on engineer. You will work closely with customer teams throughout implementation, from architecture and deployment through production operations. Your work will provide feedback that directly influences the evolution of the Anyscale platform.

In this role, you will:

• Design and implement production-grade AI platform architectures on Kubernetes and public cloud infrastructure (AWS, Azure, and GCP).
• Partner directly with customer platform, infrastructure, and ML engineering teams to deploy, operate, and optimize distributed AI workloads.
• Lead implementation engagements that include platform installation, networking, security, observability, scaling, upgrades, and operational readiness.
• Troubleshoot complex distributed systems issues spanning infrastructure, Kubernetes, networking, storage, and AI applications.
• Develop automation, tooling, reference implementations, and infrastructure-as-code that accelerate customer success and improve repeatability.
• Build trusted relationships with technical leaders, platform teams, and executive stakeholders, translating business objectives into robust technical solutions.
• Collaborate closely with Product and Engineering to communicate customer requirements, identify product improvements, and shape future platform capabilities.
• Share best practices through technical documentation, architecture guidance, workshops, and enablement.

We'd love to hear from you if you have:

• 5+ years of experience in cloud infrastructure, platform engineering, DevOps, Site Reliability Engineering, or software engineering.
• Experience building, deploying, or operating ML/AI platforms that support model training, inference, or large-scale data processing workloads.
• Strong expertise with Kubernetes and containerized production environments.
• Experience operating cloud infrastructure on AWS, Azure, or GCP, including networking, security, IAM, storage, and infrastructure automation.
• Experience with Infrastructure as Code and modern DevOps tooling such as Terraform, Helm, GitOps, CI/CD pipelines, or similar technologies.
• Strong software engineering skills in Python, Go, Java, or a comparable language, with experience building automation or production services.
• Experience working directly with enterprise customers in consulting, professional services, field engineering, solutions architecture, or another customer-facing engineering role.
• Excellent communication skills and the ability to work effectively with both executive and deeply technical stakeholders.
• Familiarity with distributed computing frameworks such as Ray, Spark, Dask, or Kubernetes-native distributed systems is a strong plus.
• A passion for solving difficult customer problems and building reusable technical solutions.
• Willingness to travel as needed to work alongside strategic customers.