Cornelis Networks, Inc.

Cornelis Networks, Inc.

Senior AI Productivity Engineer

Santa Clara, California, United States · Senior · Full-time

Sponsorship not specifiedDetected 16 hours ago
PythonBashFastAPIGitPostgreSQLRedisDockerCI/CDLinuxPlatform EngineeringAPI DevelopmentRESTLLMsRAGAgentic AIA/B TestingJiraConfluenceEmbedded SystemsResearchLeadershipMicrosoft Teams

Stay score

odds of building a lasting career here

37Risky
Cap-exempt (no lottery)0
Sponsors this role80
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70

Thin sponsorship signal and lottery-bound. A low-probability bet with your clock running. Prioritize cap-exempt roles and proven entry-level sponsors first.

Lottery odds assume a STEM candidate.

Personalize to your clock →

Community outcomes

No reports yet — be the first to help the next applicant.

About the role

  • Our differentiated architecture integrates hardware, software, and system-level technologies to maximize the efficiency of GPU, CPU, and accelerator-based compute clusters at scale.
  • As a global organization, our team spans multiple U.S. states and six countries, and we continue to expand with exceptional talent in onsite, hybrid, and remote roles.
  • This is an emerging role at the intersection of software engineering, developer platforms, infrastructure, workflow automation, and applied AI.

Responsibilities

  • Own the AI Platform: Own the configuration, tooling, and infrastructure that gives every Cornelis engineer a private, domain-aware AI assistant.
  • Build Out the Agent Workforce: Design, implement, and improve a growing workforce of autonomous agents that automate engineering operations.
  • Design and Implement New Agents: Take agents from concept to production: FastAPI REST API, CLI interface, and chat integration.
  • Work with engineering teams to identify the highest-value automation opportunities, define the agent's behavior, and build it to the platform's standards - deterministic where possible, LLM-powered where it adds real value, and cost-conscious throughout.
  • Maintain and Improve the Infrastructure: Keep the platform running reliably: containerized services on Linux, reverse proxies, systemd timers, PostgreSQL and Redis, secrets management, and enterprise integrations with GitHub, Jira, Confluence, and Microsoft Teams.
  • Debug infrastructure issues, manage deployments, and harden the platform as it grows.
  • Design agents that are reliable and grounded - not impressive in a demo but wrong in production.
  • Build CI/CD Validation Pipelines: Build and maintain automated validation that catches bad configurations, leaked credentials, and broken agent contracts before they land.
  • Lead the inner-source contribution process - review PRs from engineers across teams, enforce standards, and make sure new additions are robust and cost-effective.
  • Manage Cost and Value Across the Platform: Track what the platform costs and what it delivers.

Requirements

  • B.S. or M.S. in Computer Science, Engineering, or a related discipline, or equivalent practical experience.
  • Familiarity with software development fundamentals, including version control, testing, debugging, and code review.
  • Ability to explain how MCP was used to connect an AI system to tools or external systems.
  • Experience should go beyond simple prompt experimentation, basic chatbots, or using an AI assistant to generate text.
  • Familiarity with ingestion, chunking, embeddings, vector search, metadata, source context, or response evaluation is valuable.

Nice to have

  • Strong candidates will also have experience with several of the following:
  • Shell scripting and automation
  • CI/CD pipelines and automated validation
  • Docker and/or Podman
  • REST API development and integration
  • Workflow automation across developer or enterprise systems
  • FastAPI or a similar Python web framework
  • GitHub, Jira, Confluence, Microsoft Teams, or comparable APIs

Skills

  • Author and tune the structured workflows and system instructions that make AI agents useful for deep engineering work.
  • Build CI/CD Validation Pipelines:
  • Manage Cost and Value Across the Platform:
  • Help engineers use AI tools effectively without wasting compute on low-value work.
  • Report on platform value in terms engineering leadership can act on.
  • Track the AI Landscape and Keep the Platform Current:
  • The tooling landscape is moving fast.
  • Bring recommendations to engineering leadership with clear reasoning on capability and cost.

Compensation

  • We offer a competitive compensation package that includes equity, cash, and incentives, along with health and retirement benefits.

Company info

  • Our solutions help customers push the boundaries of AI and HPC by eliminating bottlenecks and enabling massive-scale training, inference, simulation, and data-intensive workloads.
  • This discipline is still emerging, and we are more interested in your engineering fundamentals, creativity, implementation skills, curiosity, and potential to grow into broader platform and architectural responsibility.
  • The platform routes work across a tiered model fleet; knowing when to use a lightweight model versus a heavy one is part of the job.
  • Each new agent you build becomes a permanent part of how the engineering organization operates.
  • knowing when to use a lightweight model versus a heavy one is part of the job.
Apply directly at Cornelis Networks, Inc. →Create a free account for alerts like thisView Cornelis Networks, Inc. immigration profile

This listing is sourced directly from Cornelis Networks, Inc.'s careers page and normalized into a canonical job model.