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ML Infrastructure Engineer (AI)

221 000 - 260 000$
Формат работы
hybrid
Тип работы
fulltime
Грейд
senior/principal
Английский
b2
Страна
US
Вакансия из списка Hirify.GlobalВакансия из Hirify Global, списка международных tech-компаний
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Описание вакансии

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TL;DR
ML Infrastructure Engineer (AI): Building and optimizing scalable inference infrastructure for machine learning models with an accent on Kubernetes orchestration, model serving, GPU utilization, and low-latency performance. Focus on designing distributed systems, developing model API orchestration, and scaling compute-heavy AI workloads across research and product engineering.

Location: Hybrid, SF Office; compensation and benefits are structured for U.S. geographic markets.

Salary: $221K–$260K base pay, with compensation varying by U.S. market and job-related experience.

Company

hirify.global develops an AI-powered healthcare platform that converts patient-clinician conversations into structured clinical notes and integrates with electronic medical records.

What you will do

  • Design, deploy, and maintain scalable Kubernetes clusters for AI model inference and training.
  • Develop and optimize model-serving infrastructure for high performance and low latency.
  • Scale backend infrastructure for AI products, including model deployment, throughput, and compute efficiency.
  • Optimize compute-heavy workflows and GPU utilization for machine learning workloads.
  • Build a robust model API orchestration system.
  • Define infrastructure scaling strategies with leadership as the company grows.

Requirements

  • 5+ years of experience building and deploying machine learning models in production.
  • Deep understanding of container orchestration and distributed systems architecture.
  • Expertise in Kubernetes administration, including custom resource definitions, operators, and cluster management.
  • Experience developing APIs and managing distributed systems for batch and real-time workloads.
  • Excellent communication skills for collaboration across research and product engineering.

Nice to have

  • Experience with NVIDIA Triton Server, vLLM, TRT-LLM, PyTorch, TensorFlow, or distributed training and inference libraries.
  • Familiarity with GPU cluster management and CUDA optimization.
  • Knowledge of Terraform, Ansible, GitOps, container registries, image optimization, and multi-stage builds.
  • Experience orchestrating ASR or LLM models for generative AI applications.

Culture & Benefits

  • Flexible work hours and an inclusive, supportive environment focused on empathy for clinicians and patients.
  • Paid holidays, flexible PTO for salaried employees, and paid parental leave.
  • Medical, dental, and vision coverage, HSA contributions, FSA access, and commuter benefits.
  • 401(k) matching, personal device allowance, lifestyle wallet, and mental health support.
  • Paid sabbatical after five years and equity grants for full-time employees.

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