Infrastructure Engineer
Fractal Analytics
IndiaPosted Discovered via the company's careers siteMatch locked
full time5+ years exp
About the role
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Role Details
Company: US-headquartered AI infrastructure company
Location: San Francisco, California
Employment Type: Full-time
Work Arrangement: In-office, five days per week
Experience: 5+ years in infrastructure engineering
Base Salary: $350,000-$450,000
Visa: Transfers possible
Relocation: Considered for exceptional candidates
About the Company
An innovative, venture-backed AI company building high-performance infrastructure for serving large language models (LLMs). Its platform delivers ultra-fast inference across multiple accelerator architectures and cloud environments, supporting demanding AI research and engineering workloads.
About the Role
The company is seeking a senior infrastructure engineer to own and scale its production LLM inference platform. This is a highly technical, hands-on role focused on Kubernetes, GPU infrastructure, multi-cloud deployments, and reliability for latency-sensitive AI workloads.
Responsibilities
- Build and operate production Kubernetes infrastructure across AWS, GCP, and GPU cloud providers.
- Optimize and troubleshoot LLM inference serving systems and GPU infrastructure.
- Debug NVIDIA drivers, CUDA, NCCL, and high-performance networking issues.
- Support deployments across NVIDIA GPUs and other accelerator architectures.
- Build observability, automated failover, and reliability systems.
- Collaborate with model optimization engineers to improve inference performance.
- Own complex production issues and drive solutions independently.
Requirements
- 5+ years of infrastructure engineering experience, currently at Staff level or above in an IC role.
- Strong production Kubernetes experience across multiple cloud environments.
- Hands-on experience with production LLM inference platforms.
- Expertise in NVIDIA GPU systems, CUDA, NCCL, and node-level debugging.
- Experience with inference frameworks such as vLLM, SGLang, TensorRT-LLM, or Dynamo.
- Knowledge of high-performance networking, such as InfiniBand, RDMA/EFA, or MPI.
- Experience with observability and reliability for latency-sensitive services.
Interview Process
- Initial screening call.
- Technical interview.
- Additional technical assessment, if required.
- Final interview, potentially onsite.
Not included in the source posting: benefits.
Skills
llmartificial-intelligencekubernetesnode-jsawsexpressgcp
Who can apply
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