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NeoSigma

Autonomous ML engineer automating ML workflows

Infrastructure Engineer

Platform Engineering +1Updated on Nov 13
Full-timeSan Francisco, CA, USAIn-person

About the Job

Requirements

  • Experience shipping production infrastructure at a startup and owning its operational consequences.
  • Strong fundamentals in Google Cloud Platform, Kubernetes, and Terraform.
  • Experience with enterprise deployment patterns, including bring-your-own-cloud deployments, virtual private cloud isolation, single sign-on, SAML, SCIM, and compliance.
  • Ability to assess blast radius, attack surface, and audit trails.
  • Ability to write technical design documents and operational runbooks.
  • Bachelor's degree in computer science, engineering, or a related field, or equivalent practical experience.

Responsibilities

  • Design and ship enterprise-grade deployment architecture across Google Cloud Platform, bring-your-own-cloud, and virtual private cloud-isolated environments for regulated customers.
  • Own data-retention policies end-to-end, including minimal collection, defined time-to-live periods, personally identifiable information handling, and data residency by default.
  • Build private and secure connectivity solutions, including virtual private cloud peering, PrivateLink, and Private Service Connect for customer-managed environments.
  • Own critical continuous integration and continuous delivery infrastructure, including pipelines, deployment automation, and rollout and rollback tooling across the full stack.
  • Architect security, including identity and access management, secrets management, least-privilege access, encryption, and audit logging by default.
  • Own enterprise deployment relationships, including single sign-on, SAML, SCIM, network configuration, and compliance documentation for SOC 2, HIPAA, and GDPR.
  • Build and maintain observability, including structured logging, metrics, alerting, and on-call practices across distributed deployments.

About the Company

NeoSigma

Autonomous ML engineer automating ML workflows

NeoSigma builds an AI product lab behind an intelligent automation layer called NEO. NEO acts as an autonomous ML engineer that handles the full machine learning workflow: data science tasks, model training, fine-tuning large language models, building retrieval-augmented generation pipelines, running evaluations, and deploying production-ready AI systems. It works by using multiple agents in parallel inside a GPU sandbox to run hundreds of experiments, measure their performance against targets, and pick the best models. Users can interact with NEO via a chat interface to guide exploration and provide context. NeoSigma can fix training pipelines, add new AI features to existing projects, and check for data leakage in pipelines. The goal is to connect customers, products, and AI systems by delivering automated, end-to-end ML development that speeds up building and deploying AI-powered products.

Company size
1-10
Company stage
Not listed
Total funding
Not listed
Headquarters
San Francisco, CA
Founded
2025

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