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This listing was closed on Jul 23, 2026.
Browse Open JobsMachine Learning Infrastructure Engineer
Summary by AIEngineer.careers
Machine Learning Infrastructure Engineer is a AI Infrastructure Engineer role (full-time). with 艾斯特拉股份有限公司 Astera Labs Taiwan Limited. in CALIFORNIA, US. Compensation shown: $140K–$165K. Imported listing (source: cake.me). Apply on the employer's site (cake.me).
Imported listing (source: cake.me) · Apply on cake.me
CALIFORNIA · US
$140K–$165K
Full-time
MID
The sections below reproduce the third-party job description for reference. AIEngineer.careers does not write or control this text.
Imported job description
Sourced from cake.me
Astera Labs is hiring a Machine Learning Infrastructure Engineer to build the runtime, platform, and operational backbone for modern AI systems. This role focuses on model access layers, routing, serving, telemetry, observability, evaluation infrastructure, and controls to make fast-moving AI reliable.
Responsibilities
- Build and improve internal AI infrastructure for LLM applications, agents, retrieval systems, and model-backed engineering workflows.
- Own inference deployment paths across managed and self-serve environments, including access control, monitoring, and operational reliability.
- Build platform layers such as model gateways, routing, runtime integrations, telemetry, and controls for safe execution at scale.
- Develop AI Ops capabilities across evaluation, release readiness, observability, incident triage, regression detection, and cost monitoring.
- Build dashboards, tracing, logging, and alerting for production AI systems, including spend and usage visibility.
- Improve performance and unit economics through routing, caching, batching, failover, and latency/cost optimization.
- Create reusable APIs, SDKs, and platform abstractions for easier deployment, evaluation, governance, and operation.
Requirements
艾斯特拉股份有限公司 Astera Labs Taiwan Limited
San Jose · US · 900+ employees
Astera Labs is a semiconductor company specializing in connectivity solutions for AI and cloud infrastructure. Its technology addresses data, memory, and networking bottlenecks in high-performance computing environments by providing hardware such as integrated circuits, boards, and modules.
