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Lambda

Cloud-based GPU services for AI training

Senior Systems Engineer - Enterprise AI Platforms

System AdministrationConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • At least 6 years of experience in software engineering, site reliability engineering, or IT systems engineering, building and operating systems in code rather than only configuring them.
  • Ability to design and deploy low-code/no-code and custom internal platforms integrated with large language model workflows to automate IT operations.
  • Ability to deploy, monitor, and optimize internal AI proxy gateways, including routing model traffic, managing API rate limits, optimizing latency, and controlling inference costs.
  • Ability to integrate AI agents into internal IT workflows such as messaging bots, incident triage, log parsing, user provisioning, and ticket routing.
  • Experience managing deployments, serverless functions, and infrastructure pipelines to maintain 99.9% uptime for internal systems.
  • Ability to implement guardrails, data-privacy controls, and secret management to protect sensitive corporate data from exposure to third-party large language model providers.
  • Ability to collaborate and communicate asynchronously and document issues and solutions.

Responsibilities

  • Design, write, and deliver software and services that improve the availability, scalability, reliability, and efficiency of internal IT systems and platforms.
  • Solve problems involving mission-critical services and build automation to prevent recurrence, aiming to automate responses to all non-exceptional events.
  • Work with engineering and internal teams to influence and create designs, architectures, standards, and methods for large-scale distributed systems.
  • Perform service capacity planning and demand forecasting, software performance analysis, and system tuning.
  • Produce documentation and related artifacts for the systems under the role’s responsibility.

Desired Qualifications

  • Experience or interest in machine learning/artificial intelligence workloads and compute.
  • Practical experience implementing and managing paging, alerting, and on-call scheduling flows.

About the Company

Lambda

Cloud-based GPU services for AI training

Lambda Labs provides cloud-based GPU services for AI training and inference via its AI Developer Cloud, powered by NVIDIA GH200 Grace Hopper hardware to efficiently train large language models and generative AI. Customers access on-demand or reserved GPUs and are billed by the hour (for example, NVIDIA H100 at about $1.99/hour); it also offers Lambda Echelon, where clients own infrastructure but pay a monthly hosting and support fee, and Lambda Stack, a software suite that helps ML teams install and upgrade tools like PyTorch, TensorFlow, CUDA, cuDNN, and NVIDIA drivers. The company differentiates itself through competitive pricing, available high-end hardware, and an integrated ML software stack that streamlines workflows. Its goal is to enable scalable, affordable AI development and deployment by providing flexible GPU access, reliable hosting, and easy software deployment for teams working with large models.

Company size
501-1,000
Company stage
Late Stage VC
Total funding
$9.1B
Headquarters
San Jose, CA
Founded
2012

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