Staff Software Engineer, Platform Infrastructure

About the role

The role defines the technical direction for distributed infrastructure that runs Ray workloads. The position ensures that the infinite laptop vision operates at scale for developers and data scientists.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

Architectural foundations are shaped into a multi-year roadmap that controls Ray cluster orchestration across cloud and on-premises environments.

High-performance control plane components are designed and optimized to serve large-scale, heterogeneous AI and machine learning workloads.

Reliability standards are established organization-wide to govern scalability and observability of managed infrastructure.

Long-term strategy for accelerators and container management directs how distributed workloads execute efficiently on GPUs and TPUs.

Market requirements from ML experts and customer teams are translated into infrastructure foundations through cross-functional collaboration.

Requirements

You bring 5+ years of experience writing high-quality production code and leading complex distributed systems projects.

You have a proven record designing and maintaining highly available, scalable, and secure cloud-native platforms on AWS, Azure, or GCP.

You possess deep expertise in Kubernetes-based deployments and container orchestration at massive scale.

You apply advanced knowledge of the Linux kernel, networking, and low-level operating system foundations.

You master Go and Python, and you set coding standards and best practices for the engineering team.

You demonstrate the ability to mentor senior engineers, influence technical direction without direct authority, and navigate complex trade-offs.

You hold US work authorization as required by the position.

You hold a Bachelor's degree or equivalent experience as a baseline qualification.

Practical notes

Anyscale is an Equal Opportunity Employer where candidates are evaluated without regard to protected characteristics.

Anyscale participates in E-Verify for employment eligibility verification.

Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Ray powers distributed computing for many well-known companies and runs in diverse environments.

The role focuses on cloud-native infrastructure, Kubernetes, and systems programming in Go and Python.

Distributed systems, reliability, and accelerator integration define the day-to-day work.

Mentoring, cross-team alignment, and community interaction are central to the position.

The infrastructure team owns control plane and data plane components that underpin scalable ML workloads.

Questions to ask

Useful questions for the interview: what a typical week looks like, how work is assigned, what tools the team uses, and how feedback works. Asking how the role has changed recently and what the team wishes it had known when joining is also reasonable. Questions about the manager's priorities are especially valued.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.