Site Reliability Engineer, Platform Infrastructure (Foundations)
Anyscale is hiring a Site Reliability Engineer, Platform Infrastructure (Foundations) for a remote role open to applicants in United States. It pays $200k-$240k a year and Level rates it ; you can apply on Anyscale.
Level's read on this job
- Builds AI ●●●● is how Level rates this role: the job is building AI systems.
- The posting lists this role as remote.
- Tools and skills the posting cites include OpenAI, Kubernetes and Ray.
- Level classifies it as a senior role.
- Level lists 20 other open roles at Anyscale.
AI in this role
Design and scale cloud infrastructure and control plane services for distributed AI/ML workloads on Ray.
About Anyscale:
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:
Anyscale is looking for a Site Reliability Engineer to join the Infrastructure team. Anyscale aims to provide the next generation of tools and infrastructure to make developing and running distributed AI applications in the cloud as easy as on your laptop. As part of the Infra team, we build the scalable, secure, and robust backbone that enables this vision.
Our team is responsible for both the control plane, which orchestrates cluster management, scheduling, and user access, and the data plane, which ensures high-performance execution of distributed workloads.
We are seeking a talented engineers with a strong background in control plane and data plane development, along with expertise in Kubernetes, container orchestration, and cloud-native infrastructure. You will play a crucial role in designing, implementing, and optimizing the critical infrastructure that powers Anyscale’s cloud platform.
You will have the opportunity to work on open-source Ray, contribute to our infinite laptop proprietary product, and develop seamless integration between the two, while also delivering high-impact features for our customers.
A snapshot of projects you may work on
Design, build, and scale services that orchestrate Ray clusters across cloud and on-prem environments, supporting both VM-based and Kubernetes-based deployments
Optimize control plane components for large-scale, distributed AI/ML workloads
Build intelligent scheduling and resource management systems for heterogeneous compute clusters
Develop features to enhance the reliability, performance, scalability, and observability of Anyscale-managed Ray workloads
Support and optimize accelerator integration (e.g., GPUs, TPUs).
Handle container image management and dependency resolution for distributed workloads
Participate in code reviews, design and architecture discussions
Provide on-call support, working closely with customer and field teams to troubleshoot infrastructure issues
Collaborate with leading distributed systems and machine learning experts to push the boundaries of AI infrastructure
We'd love to hear from you if have
Bachelor's degree in Computer Science, Engineering, or equivalent practical experience
3+ years of experience writing high-quality production code
Hands-on experience in building and maintaining highly available, scalable, and performant distributed system
Expertise in cloud-native technologies (AWS, Azure, GCP) and Kubernetes-based deployments
Deep understanding of networking, security, and authentication mechanisms in cloud environment
Familiarity with observability stacks (Prometheus, Grafana etc)
Proficiency in Go and Python
Knowledge of low-level operating system foundations (Linux kernel, file systems, containers)
Anyscale Inc. is an Equal Opportunity Employer. Candidates are evaluated without regard to age, race, color, religion, sex, disability, national origin, sexual orientation, veteran status, or any other characteristic protected by federal or state law.
Anyscale Inc. is an E-Verify company and you may review the Notice of E-Verify Participation and the Right to Work posters in English and Spanish
How we rate this
Site Reliability Engineer, Platform Infrastructure (Foundations) at Anyscale rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- Tell me about a project where distributed systems was part of your work. What did you do?
- Tell me about a project where sre was part of your work. What did you do?
- Tell me about a project where infrastructure was part of your work. What did you do?
- Tell me about a project where control plane was part of your work. What did you do?
- Tell me about a project where container orchestration was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: Distributed Systems, Sre, Infrastructure, Control Plane, and Container Orchestration. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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