Ovii Job Board

Software Engineer (Ray Data)

Anyscale

San Francisco, San Francisco • Onsite - San Francisco, San Francisco • Full-Time • 3+ years

Posted 2026-07-10 USD 215,000 - USD 230,000 per year Tech & Engg

Apply on employer site

Job Description

Ovii's Interpretation of the Role

Software Engineer on the Ray Data team building a Python‑native data processing engine that powers large‑scale AI workloads. You will improve performance, ensure fault‑tolerant scaling, and collaborate with customers to accelerate their machine‑learning pipelines.

Role Snapshot

  • Build and optimize the Ray Data engine
  • Scale data pipelines for AI/ML workloads
  • Ensure fault‑tolerant distributed systems
  • Collaborate with customers on AI scaling
  • Focus on performance and stability

Must-Have Requirements

  • Python
  • Distributed systems design
  • Data processing / database internals
  • Performance optimization
  • Fault tolerance
  • building scalable fault‑tolerant distributed systems
  • data processing and database internals

Nice-to-Have Signals

  • Multi‑modal data processing experience

Work Setup

  • Location: San Francisco, USA
  • Work mode: ONSITE
  • Employment type: Full-Time

Eligibility Gates

  • Visa sponsorship: unknown

Not Specified in JD

  • Salary range
  • Visa sponsorship
  • Remote eligibility
  • Education requirement
  • Certifications
  • Relocation
  • Travel
  • Coding test
  • Portfolio
  • GitHub
  • Cover letter

What You'll Likely Work On

  • Improve performance of Ray Data and multi‑modal batch inference use cases
  • Ensure efficient scaling across heterogeneous data‑pipeline stages
  • Build data‑loading solutions for production training workloads
  • Maintain stability and fault tolerance at high scale
  • Partner with AI‑native customers to scale their workloads

Good Fit If You Have

  • Passion for large‑scale AI systems
  • Experience with multi‑modal data processing
  • Familiarity with the Ray ecosystem

Skills

  • Python
  • Distributed systems design
  • Data processing pipelines
  • Performance optimization
  • Fault tolerance
  • Scalable ML workloads