Infrastructure Tech Lead

Posted Yesterday
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San Francisco, CA, USA
In-Office
Expert/Leader
Artificial Intelligence • Logistics • Machine Learning • Software
The Role
Own infrastructure for deploying and operating customer-specific AI models and services. Responsibilities include AWS cloud and GPU resource management, CI/CD and infrastructure as code, customer data isolation and security, SOC 2 compliance, monitoring and logging, ETL pipelines, model versioning, ML lifecycle management, and automated testing. The role requires strong Python skills, infrastructure or platform engineering experience, startup experience, and technical leadership.
Summary Generated by Built In
Infrastructure Tech Lead / Principal Engineer

Omnifold trains custom AI models that help planners forecast the future. We are hiring our first infrastructure tech lead, who will own the systems that make everything else possible.

What makes this job interesting:

  • We train a unique model for each customer, which means model training and inference work differently here than at any other company. You’ll never get more reps building model training infrastructure!

  • Our team has very fast iteration speed but needs robust monitoring to pick up signal on user patterns. This is especially important as our application interface for AI-driven forecasting is unique on the market.

What you’ll own

  • Deployment: Reliable processes for getting models and services into production

  • Security: Data isolation between customers, product security, infrastructure hardening (SOC2 compliance and beyond)

  • Cloud resource management: GPU allocation, instance sizing, cost optimization

  • Monitoring and logging: Visibility into what's running, what's failing, and why

  • Data and ML ops: ETL pipelines from varied customer data sources, model versioning and lifecycle management

  • Automated testing: Building the test infrastructure that lets us ship with confidence

What we’re looking for

  • Experience with cloud computing (especially GPU workloads), CI/CD infrastructure-as-code. We run on AWS

  • Familiarity with or interest in ML workflows

  • Security fundamentals: encryption, access controls, compliance basics

  • Python proficiency

  • Ideally ~10 years of experience, including startup experience, with at least 3 years in a tech lead role. 5+ years in infrastructure, DevOps, or platform engineering roles

  • Must have a strong Computer Science background

Location: San Francisco (in-person, 5 days per week)

Omnifold’s Mission

Every bad forecast has a physical consequence. Unnecessary goods are manufactured, shipped, and stored. Emergency air freight is needed for misallocated products. Poor production planning means workers show up with nothing to do, or work frantic overtime. Inefficiency is everywhere.

Our mission is to eliminate waste and accelerate growth for every company with physical products.

Skills Required

  • Experience with cloud computing, especially GPU workloads
  • Experience with CI/CD infrastructure as code
  • AWS experience
  • Familiarity with or interest in machine learning workflows
  • Knowledge of security fundamentals, including encryption, access controls, and compliance basics
  • Python proficiency
  • Approximately 10 years of professional experience
  • At least 3 years of experience in a technical lead role
  • At least 5 years of experience in infrastructure, DevOps, or platform engineering
  • Startup experience
  • Strong computer science background
  • Ability to work in person in San Francisco five days per week
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The Company
17 Employees

What We Do

Omnifold develops AI-powered planning software for complex supply chains and commercial operations. Its platform combines internal, external, and customer-specific data to model procurement, manufacturing, distribution, marketing, and sales dynamics, then uses prediction, optimization, language, and reasoning to improve forecasting. The system adapts to market changes and discovers growth strategies, helping businesses improve margins, cash flow, and operational decisions across complex enterprises.

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