Founding Engineer – Full Stack ML DevTools & Systems

Location: San Francisco, CA

Type: Full-Time

Base Compensation: $150,000 – $250,000

Equity: Competitive Series A Equity Package

Overview

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
• Run reinforcement fine-tuning workflows
• 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.

Core Responsibilities

Platform & Backend 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

Python SDK & Developer Experience

• 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

Infrastructure & Cloud Systems

• Work across compute, networking, storage, and IAM configurations
• Design systems that are scalable, reproducible, and secure
• Collaborate on distributed systems design and execution infrastructure

Customer & Research Collaboration

• 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

Requirements

• 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
• Cloud fundamentals: compute, networking, storage, IAM
• Strong product instincts with a bias toward shipping
• Demonstrated end-to-end ownership of production systems

Required Candidate Q&A

• LinkedIn Profile
• GitHub URL
• Publications URL (Google Scholar or similar, if applicable)

Interview Process

• Initial Screen
• Technical Evaluation
• Work Trial
• Final Discussion
• Offer Decision