Founding Engineer – Full Stack ML DevTools & Systems

Founding Engineer – Full Stack ML Dev Tools & Systems

Location: San Francisco, CA
Type: Full‑Time
Base Compensation: $150,000 – $250,000
Equity: Competitive Series A Equity Package

This is a founding‑level engineering role within a Series A AI infrastructure company building core developer tools and platform primitives for post‑training, evaluation, and reinforcement learning workflows.

The platform enables ML engineers and researchers to:

Create structured training data

Evaluate model performance reliably and reproducibly at scale

This is a high‑ownership role at the center of the product. You will operate across the Python SDK, backend systems, infrastructure, and developer experience—partnering directly with frontier labs, enterprise AI teams, and AI‑native startups.

This is not a narrow feature role. You will shape foundational platform architecture and developer workflows that power advanced model training systems.

Design and implement backend systems supporting post‑training workflows, dataset primitives, run tracking, and artifact management

Build reliable execution and orchestration systems with strong isolation and reproducibility

Improve observability, debugging capabilities, and performance across job execution and distributed data pipelines

Contribute to containerized infrastructure and Kubernetes‑based deployment patterns

Own and evolve the Python SDK with clean APIs, strong documentation, intuitive defaults, and extensibility

Design developer‑friendly abstractions for reinforcement learning, evaluation loops, and training workflows

Develop evaluation‑native workflows connecting capability measurement, data creation, training, and re‑evaluation loops

Improve CLI tools, developer interfaces, and local‑to‑cloud workflows

Work across compute, networking, storage, and IAM configurations

Design systems that are scalable, reproducible, and secure

Collaborate on distributed systems design and execution infrastructure

Partner directly with ML engineers and researchers to translate real‑world workflows into platform improvements

Incorporate structured customer feedback into roadmap decisions

Operate at the intersection of research needs and production reliability

Strong production experience in Python

Comfort operating across the stack, including APIs, backend systems, data systems, and frontend integration

Deep understanding of Docker and Linux environments

Strong product instincts with a bias toward shipping

Demonstrated end‑to‑end ownership of production systems