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NeoSigma

Autonomous ML engineer automating ML workflows

Infrastructure Engineer

Full-TimeUpdated on 10/20/2026
No salary listed
Mid
Bachelor's
Bengaluru, Karnataka, India
In Person
DevOps & InfrastructurePlatform Engineering, Cloud Engineering

About the job

Requirements
  • Experience shipping infrastructure at a startup and owning the consequences of those systems.
  • Strong fundamentals in cloud infrastructure, including Google Cloud Platform, Kubernetes, and Terraform.
  • Experience with enterprise deployment patterns, including bring your own cloud, virtual private cloud isolation, single sign-on, Security Assertion Markup Language, System for Cross-domain Identity Management, and compliance.
  • A security-first approach that considers 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 deployment infrastructure, including pipelines, deployment automation, and rollout and rollback tooling across the full stack.
  • Architect security from the ground up, including identity and access management, secrets management, least-privilege access, encryption, and audit logging by default.
  • Own enterprise deployment relationships, including single sign-on, Security Assertion Markup Language, System for Cross-domain Identity Management, 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 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

N/A

Total Funding

N/A

Headquarters

San Francisco, California

Founded

2025

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Simplify's Take

What believers are saying

  • NeoSigma published three technical posts between March and September 2026, proving fast execution.
  • The company claims backers include Jeff Dean, OpenAI, DeepMind, Mercor, Decagon, and World Labs leaders.
  • Open roles across San Francisco and Bengaluru show expansion into engineering, product, and GTM.

What critics are saying

  • Daytona's February 2026 Series A and Cloudflare's April 2026 sandbox entry squeeze NeoSigma's moat.
  • NeoSigma's public footprint stays tiny: LinkedIn shows 1-10 employees, founded 2025.
  • If agents stop needing custom feedback infrastructure, NeoSigma's core product becomes irrelevant by 2027.

What makes NeoSigma unique

  • NeoSigma targets production feedback loops, not generic model training, as of September 2026.
  • Its July 17, 2026 Workspaces article shows isolated sandbox infrastructure for autonomous agents.
  • Gauri Gupta markets NEO as an autonomous ML engineer for end-to-end workflow automation.

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Benefits

  • Health Insurance
  • Paid Vacation
  • Flexible Work Hours
  • Company Equity