Forward Deployed Engineer AI ML Platforms
Anyscale is an AI compute platform built by Ray’s creators for distributed data processing, model training, inference, and related production AI workloads.
About Anyscale
Anyscale provides a managed, multi-cloud platform for building, running, and governing distributed AI workloads with Ray, including data processing, training, and model serving.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will partner directly with customer platform, infrastructure, and ML engineering teams to design, deploy, operate, and optimize production AI platforms. You will lead implementations from architecture through production operations, resolve complex distributed-systems issues, develop reusable automation, and share technical guidance and best practices.
Requirements
- 5+ years of experience in cloud infrastructure, platform engineering, DevOps, site reliability engineering, or software engineering
- Experience building, deploying, or operating ML or AI platforms for model training, inference, or large-scale data processing
- Expertise with Kubernetes and containerized production environments
- Experience operating AWS, Azure, or GCP infrastructure, including networking, security, IAM, storage, and automation
- Experience with infrastructure as code and DevOps tooling such as Terraform, Helm, GitOps, or CI/CD pipelines
- Software engineering skills in Python, Go, Java, or a comparable language
- Experience in customer-facing engineering roles with enterprise customers
- Communication skills for executive and technical stakeholders
Responsibilities
- Design and implement production-grade AI platform architectures on Kubernetes and public cloud infrastructure
- Partner with customer engineering teams to deploy, operate, and optimize distributed AI workloads
- Lead implementation engagements covering installation, networking, security, observability, scaling, upgrades, and operational readiness
- Troubleshoot distributed systems issues across infrastructure, Kubernetes, networking, storage, and AI applications
- Develop automation, tooling, reference implementations, and infrastructure as code
- Build relationships with technical leaders, platform teams, and executive stakeholders
- Communicate customer requirements and product improvements to Product and Engineering
- Share best practices through documentation, architecture guidance, workshops, and enablement
- Travel as needed to work with strategic customers
Benefits
- Stock options
- Healthcare plans with 95% of premiums covered
- 401k retirement plan
- Wellness and education stipend
- Paid parental leave
- Fertility benefits
- Paid time off
- Commute reimbursement
- Office lunch