Founding Engineer

Founding Engineer — AI / Machine Learning Software and Infrastructure

Location: San Francisco

Compensation: $180K–$250K Base + Equity

Founding Engineer

A well-funded, early-stage AI-native company is seeking a Founding Engineer to build the core technical software and infrastructure underlying a new generation of data-intensive, machine-learning-driven products.

This is a high-impact founding engineering role for someone who has built sophisticated internal platforms, ML infrastructure, data systems, or AI-powered developer and operational tooling from the ground up.

The company has raised significant institutional capital and is building an intentionally small, highly technical team. This engineer will work directly with the CEO, CTO, and senior business leadership , translating complex business requirements into scalable technical systems.

What You’ll Build

You will own the architecture and development of the company's central internal platform, including:

• Data infrastructure: Build ingestion, transformation, governance, and orchestration systems across complex and highly varied datasets.
• ML infrastructure: Build and operate model deployment, serving, monitoring, and supporting infrastructure for machine-learning models.
• Experimentation & backtesting: Develop systems that allow teams to test, validate, and iterate on new strategies before deploying capital.
• Internal AI tooling: Build configurable internal workbenches used by technical and business teams.
• Agentic AI workflows: Develop AI agents and automated workflows supporting research, analysis, validation, and decision-making.
• Platform architecture: Design reusable infrastructure that enables multiple product teams to build rapidly on a common technical foundation.
• Governance & compliance: Build auditability, data governance, controls, and compliance directly into the platform.
• Technical product leadership: Work directly with executive leadership to convert ambiguous business requirements into technical specifications and lead execution.

What We’re Looking For

The ideal candidate has previously built a highly configurable end-to-end internal platform, workbench, or operating system , rather than exclusively building customer-facing applications.

You may come from an AI lab, leading technology company, quantitative environment, or high-growth startup where you built infrastructure and workflows surrounding machine-learning products.

Strong candidates will bring experience across several of the following:

Platform Engineering • AI Infrastructure • ML Infrastructure • Machine Learning Systems • Data Engineering • Distributed Systems • Model Serving • MLOps • AI Agents • Agentic AI • LLM Infrastructure • Data Platforms • Backend Engineering • Cloud Infrastructure • Experimentation Platforms • Internal Developer Platforms • Workflow Automation

We are particularly interested in engineers who:

• Think deeply about systems architecture and scalability
• Have built infrastructure for data-intensive and model-intensive applications
• Can operate effectively in ambiguous, zero-to-one environments
• Prioritize architecture, functionality, and reliability over visual polish for internal tooling
• Have strong product instincts and can independently determine what should be built , not simply execute predefined specifications
• Are comfortable owning critical infrastructure with significant autonomy

Product management experience or significant technical product ownership at a leading technology company is a plus, but not required.

Why Join?

This is an opportunity to become one of the earliest technical hires at a heavily funded AI-native company and build foundational infrastructure from scratch.

You will have:

• Founding equity
• $180K–$250K base compensation
• Direct access to the CEO and CTO
• Ownership over core platform architecture
• The opportunity to build sophisticated AI/ML infrastructure without the bureaucracy of a large engineering organization
• Significant autonomy over technical decisions and execution

For exceptional performance, the economics of the profit-sharing structure create substantial additional upside beyond base compensation and equity.

Location

San Francisco

The company operates on an in-office model.

Requirements

Candidates must already have U.S. work authorization. Visa sponsorship is not available.

This is a rare opportunity for an elite Platform Engineer, ML Infrastructure Engineer, AI Infrastructure Engineer, Staff Software Engineer, or Founding Engineer who wants true ownership of a company's technical foundation.