Customer Success Engineer

AI inference cloud offering OpenAI-compatible APIs for machine-learning models, private deployments, and GPU infrastructure.

Series B5 lead step-downs
Palo Alto, United States
About DeepInfra

DeepInfra provides hosted AI inference for language, vision, embedding, image, video, and speech models, alongside private model deployments and dedicated GPU clusters.

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Skills

Candidate Availability

Required and preferred rules are kept separate and reflect the wording in the original posting.

About the Role

You will own technical customer issues from triage through resolution across inference workloads. You will investigate logs, metrics, dashboards, API integrations, billing, quotas, and capacity requests. You will help customers optimize platform usage and turn recurring problems into documentation, runbooks, bugs, and product improvements.

Requirements

  • 2+ years of experience in customer success engineering, support engineering, solutions engineering, technical account management, or similar roles, or software engineering experience with interest in customer work
  • Computer Science or Engineering degree or equivalent hands-on background
  • Knowledge of HTTP, REST APIs, status codes, headers, streaming responses, authentication, and integration code
  • Python or a similar programming language
  • Command-line, log, metric, and dashboard experience
  • Experience with Grafana, Prometheus, Loki, or equivalent tools
  • Written technical communication skills
  • Ability to work independently and prioritize a support queue

Responsibilities

  • Own customer technical issues from triage through resolution
  • Debug production inference issues using logs, metrics, and dashboards
  • Analyze traffic, concurrency, and token throughput for capacity requests
  • Resolve billing, usage, and quota questions
  • Advise customers on platform usage and model deployment choices
  • Plan capacity and dedicated deployments
  • File and drive bugs and feature requests
  • Create documentation, runbooks, and product improvements for recurring issues
  • Surface customer patterns to Product and Engineering