Anyscale is the company behind Ray — the open-source framework for scalable Python and AI workloads. Used by OpenAI, Uber, ByteDance, Cohere, and others to train and serve large models. The interview is technically demanding, with deep distributed-systems work and strong overlap with the AI infrastructure space.
Process
Recruiter screen → 60-minute coding pair (Python or C++) → onsite virtual: 2 coding (medium-hard), 1 system design (always distributed), 1 craft deep-dive, 1 behavioral. Senior+ candidates may get an additional architecture round. Cycle: 3–5 weeks.
What they actually ask
- Design a distributed actor system with fault tolerance and resource scheduling
- Design a parameter server for distributed ML training
- Design a serving layer for low-latency LLM inference at high QPS
- Coding: graph/tree problems, often with concurrency or distributed flavor
- Past-project deep dive: must demonstrate deep systems work
Levels and comp (2026)
- SE II: $200K–$260K total
- Senior SE: $290K–$390K
- Staff: $420K–$560K
- Principal: $600K–$800K+
Anyscale comp is in the upper tier of mid-size AI infra companies given the late-stage funding and the importance of Ray to AI ecosystem.
Prep priorities
- Be fluent in Python and at least one systems language (C++ or Rust)
- Read the Ray paper and core engineering blog posts
- Understand actor models, distributed scheduling, and the realities of running ML workloads
Frequently Asked Questions
Is Anyscale remote-friendly?
Hybrid in San Francisco; remote within US for many roles. Concentrations in Bay Area and NYC.
How does Anyscale compare to Modal or Together AI?
Modal is serverless Python; Together AI is LLM-API-focused. Anyscale is the broadest, with infrastructure for both training and serving. Comp is at the high end among the three.
Is Ray experience required?
Helpful but not mandatory. Strong distributed-systems fundamentals matter more.