Forward Deployed Engineer – SRE

Vaga não está no LinkedIn

🕒 Julho 31

🏄 California – Remoto

🏄 California – Remote

⏰ Tempo Integral

🟡 Pleno

🟠 Sênior

⛑ DevOps & Engenheiro de Confiabilidade do Site (SRE)

🦅 Patrocina Visto H1B

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Andromeda

SiteLinkedInTodas as Vagas

11 - 50 funcionários

🏥 Saúde

💼 Consultoria

🏨 Hospitalidade

🔥 Investimento no último ano

💰 $15.142.238 Series A - Andromeda Robotics em 2025-09

Healthcare • Consulting • Hospitality

A Andromeda é um serviço de computação com GPU e um marketplace que oferece acesso instantâneo a grandes clusters de aceleradores H100, H200 e B200 para experimentos, treinamentos em larga escala e inferência. Suporta orquestração com Slurm, Kubernetes ou SSH direto, oferece uso flexível sem duração mínima e preços competitivos, inclui expertise em DevOps, armazenamento local NAS ou transmitido sem taxas de entrada/saída, e suporte 24/7 com SLAs do setor. A empresa também opera um marketplace de GPUs de terceiros em gpulist. ai.

Descrição

• Serve as the primary technical point of contact for teams running large-scale training and inference workloads • Work inside customer environments to diagnose real failures: NCCL timeouts, stragglers, checkpoint I/O stalls, degraded links, OOM patterns • Profile and improve distributed training performance on live workloads • Own reliability outcomes for the accounts you're deployed on • Ensure the health and performance of high-speed interconnects (InfiniBand, RoCE, NVLink) • Lead incident response for complex, multi-layer failures spanning hardware, networking, orchestration, and ML frameworks • Turn every repeated deployment problem into automation

🎯 Requisitos

• Hands-on experience operating GPU clusters in production (NVIDIA A100/H100/H200/B200 or equivalent) • Production experience with InfiniBand, RoCE, or NVLink fabrics in the context of distributed training • Working knowledge of how large training and inference jobs actually run • Expert-level Linux experience: kernel tuning, driver management (NVIDIA drivers, CUDA toolkit), cgroup/namespace internals, container runtimes, and performance profiling at the syscall and hardware level • Strong experience running Kubernetes in production with GPU workloads • Strong engineering skills in Python, Go, or Bash • Infrastructure-as-Code proficiency (Terraform, Helm, Ansible, or equivalent) • Hands-on experience building monitoring and alerting for GPU-specific telemetry

🏖️ Benefícios

• Comprehensive benefits: for you and your dependents, including healthcare, dental, and vision coverage • 401(k) • Unlimited PTO