Machine Learning Engineer, Customer Engineering

Ovii's Interpretation of the Role

The Machine Learning Engineer, Customer Engineering partners with customers to adopt and scale Anyscale's Ray‑based platform. You will troubleshoot complex distributed ML issues, guide LLM pipeline deployments, and influence product improvements through direct customer feedback.

Role Snapshot

• Customer‑facing ML engineer
• Hybrid (San Francisco) location
• 7+ years ML experience
• End‑to‑end issue ownership
• Cross‑functional collaboration

Must-Have Requirements

• Machine Learning
• LLM pipeline development
• Distributed ML workload optimization
• Cloud platforms (AWS, GCP, Azure) & Kubernetes
• Data pipeline engineering
• Strong organization & multitasking
• Excellent communication
• Ownership & self‑motivation
• Distributed ML workloads
• Cloud infrastructure (AWS, GCP, Azure)

Nice-to-Have Signals

• Ray
• MLOps platforms
• Kubernetes (container orchestration)
• Terraform
• GitHub Actions
• Mentorship & training
• Kubernetes
• CI/CD (GitHub Actions)

Work Setup

• Location: San Francisco, United States
• Work mode: HYBRID
• Remote scope: UNSPECIFIED
• Employment type: Full-Time

Eligibility Gates

• Visa sponsorship: unknown

Not Specified in JD

• Visa sponsorship
• Salary range
• Remote eligibility
• Education requirement
• Certifications
• Relocation
• Notice period
• Travel
• Security clearance
• Coding test
• Portfolio
• GitHub
• Writing sample
• Cover letter

What You'll Likely Work On

• Resolve customer tickets and drive adoption of the Anyscale platform
• Own troubleshooting, triage, escalation, and resolution of distributed ML issues
• Participate in a follow‑the‑sun support model for high‑priority incidents
• Track bugs and feature requests, influencing product roadmap
• Create and improve internal tools, playbooks, and documentation
• Provide feedback to product and engineering teams to enhance the user experience
• Build strong technical relationships with customer stakeholders

Good Fit If You Have

• Enjoys fast‑paced, startup‑like environments
• Strong sense of ownership and self‑directed learning
• Passionate about AI/ML, LLMs, and emerging AI applications