Founding Engineer Wanted: Build Keystone’s Autonomous Coding Agent Platform​

Breaking: Keystone Seeks Founding Engineer to Build end-To-End platform for Autonomous Coding

keystone, a startup focused on the infrastructure behind autonomous coding agents, is recruiting a founding engineer to join the solo founder on the product’s core development. The role is based in person in San francisco’s SoMa district.

The company provides sandboxed environments that mirror production, integrates with event-based triggers from services like Sentry, Linear and GitHub, and supports verification workflows. The goal is to enable teams to ship code end-to-end — not merely generate code.

The position offers a competitive package designed for early-stage talent eager to shape a product from the ground up. The role includes a salary band of $150,000 to $350,000 plus equity in the range of 0.5% to 3%.

the stack driving Keystone’s product includes TypeScript, React (next.js), python, Postgres, Redis, and AWS. The founder notes that the work requires close collaboration with the founder to steer the product’s direction and execution.

Key details at a glance

Fact	Details
Company	Keystone
Role	Founding Engineer (core product)
Location	In-person in San Francisco, SoMa
Tech Stack	TypeScript, React (Next.js), Python, Postgres, Redis, AWS
Compensation	$150K–$350K salary + 0.5%–3% equity
Product Focus	Autonomous coding agents, sandboxed prod-like environments, event triggers, verification workflows

Why this matters for the future of software development

Keystone’s approach centers on turning coding agents into ship-ready systems. By offering sandboxed environments that mimic production and connecting to familiar triggers and workflows, the platform aims to move teams from code generation to reliable delivery. This model reflects a broader shift toward tooling that not only writes code but inherently verifies, tests, and deploys it in real-world conditions.

For aspiring founders and engineers, the prospect to join at the ground floor of a product focused on end-to-end delivery can accelerate learning and career growth. Early-stage equity compounds the potential upside when a product scales, especially in a market increasingly hungry for robust autonomy in software development.

Industry observers note that foundational roles in SF startups continue to attract top tech talent who value ownership,direct impact,and the chance to shape a product’s trajectory from day one. Keystone’s proposition fits that pattern, pairing a hands-on building mandate with a clear, technology-forward stack.

evergreen insights: what this signals for engineers and startups

Autonomous coding infrastructure is evolving from a niche concept to a strategic layer in software delivery. As teams seek faster iteration with reliable quality gates, sandboxes and verification workflows become essential. early hires in such ventures frequently enough gain exposure to a full stack — from frontend interfaces to backend services and deployment pipelines — accelerating expertise across disciplines.

Two reader questions to ponder: How would you leverage sandboxed environments to accelerate your own development workflow? And would joining a founding team in a high-growth tech hub align with your career goals?

Share your thoughts in the comments—do you see this model reshaping how software is built and delivered?

For more context on how autonomous engineering ecosystems are evolving, industry sources note sustained demand for engineers who can bridge product vision with practical, scalable implementation.

How do you think this kind of role will adapt as tooling and AI-assisted development mature?

Engage with us: what would you prioritize in an early-stage platform designed to ship code safely and efficiently?

Category Required Expertise Why it Matters Programming Python (3.11+), TypeScript, Go Core language for LLM integration and backend services AI/ML Prompt engineering, LLM APIs (openai, Anthropic), LangChain Drives the autonomous reasoning pipeline DevOps Docker, Kubernetes, CI/CD (GitHub Actions) Ensures scalable, isolated execution of code agents Security SELinux/AppArmor, OWASP best practices Protects host systems from generated code Data Management PostgreSQL, Redis, vector stores (FAISS, Pinecone) Stores task metadata, embeddings, and agent memory UX/UI React, figma, VS Code extension API Creates intuitive interfaces for developers

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Why Keystone’s Autonomous Coding Agent Platform Is a Game‑Changer

Keystone is building the next generation of AI‑driven growth tools, leveraging the same breakthrough technologies behind AutoGPT, Devin, and other autonomous coding agents. By chaining large‑language‑model prompts, memory layers, and shell commands via frameworks like LangChain, Keystone’s platform aims to let developers describe high‑level objectives while the agent writes, tests, and ships production‑ready code — all with minimal human supervision. The market trend highlighted in Forbes’ 2025 coverage of AI coding agents confirms rapid adoption across startups and enterprises, making this a prime opportunity for a founding engineer to shape a platform that could redefine software development pipelines.

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Core Responsibilities for the Founding Engineer

1. Architect the Autonomous Agent Core

• Design the prompt‑chaining workflow using LangChain or equivalent orchestration tools.
• Implement a robust memory system to preserve context across multi‑step tasks.

2. Develop Secure Execution Environments

• Build sandboxed containers that allow the agent to run shell commands safely.
• Integrate runtime monitoring to detect and mitigate rogue code execution.

3. Create Multi‑Modal Interaction Layers

• Enable natural‑language interfaces (CLI, web UI, VS Code extension).
• Support code‑generation, refactoring, and documentation synthesis in real time.

4. Drive Performance Optimization

• Profile LLM inference latency and implement caching strategies.
• Optimize token usage to reduce operational costs while maintaining output quality.

5. Lead Early User Feedback Loops

• Conduct beta testing with dev teams, gather actionable metrics, and iterate rapidly.
• Publish technical blog posts and open‑source utilities to grow the developer community.

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Essential Skills & Technologies

Category	Required Expertise	Why It Matters
Programming	Python (3.11+), TypeScript, Go	Core language for LLM integration and backend services
AI/ML	Prompt engineering, LLM APIs (OpenAI, Anthropic), LangChain	Drives the autonomous reasoning pipeline
DevOps	Docker, Kubernetes, CI/CD (GitHub Actions)	Ensures scalable, isolated execution of code agents
Security	SELinux/AppArmor, OWASP best practices	Protects host systems from generated code
Data Management	PostgreSQL, Redis, vector stores (FAISS, Pinecone)	Stores task metadata, embeddings, and agent memory
UX/UI	React, Figma, VS Code extension API	Creates intuitive interfaces for developers

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Benefits of Joining keystone at the Founding Stage

• equity Ownership – Direct share in a platform positioned to capture a multi‑billion‑dollar AI‑developer market.
• Technical Autonomy – Freedom to choose architecture, tools, and design patterns without legacy constraints.
• Industry Visibility – Early press coverage (e.g., Forbes 2025 AI coding agents) accelerates brand awareness.
• Cross‑Functional Collaboration – Work alongside product strategists, data scientists, and seasoned entrepreneurs.
• Impactful Innovation – Your code will power autonomous agents that can write, test, and deploy entire microservices with a single prompt.

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Practical Tips for Applicants

1. Showcase Real‑World Agent Projects

• Publish a GitHub repo where you built a mini‑AutoGPT that performs end‑to‑end code generation.
• Include documentation on prompt chains, memory handling, and sandboxing.

2. Demonstrate Performance‑Focused Mindset

• Provide benchmark results comparing token usage before/after optimization.
• Explain cost‑saving strategies for large‑scale LLM calls.

3. Highlight Security‑First Implementations

• Detail how you isolated exec environments (e.g., seccomp profiles, resource limits).
• Share any penetration‑testing outcomes or mitigations you applied.

4. Communicate Collaborative Success

• Reference participation in open‑source projects like LangChain or the AutoGPT community.
• Describe how you incorporated user feedback into iterative releases.

5. Prepare a Vision Pitch

• Draft a 2‑page roadmap outlining how you would expand Keystone’s agent capabilities over the next 12 months (e.g., multi‑language support, CI integration).

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Real‑World Example: AutoGPT & Devin Success Stories

• autogpt (2023) demonstrated that a single LLM can autonomously plan,code,and debug a full‑stack web app,reducing development cycles by up to 70 %.
• Devin (2024) extended this model with advanced memory management,enabling the agent to maintain context across weeks of iterative feature development.
• Both platforms rely on LangChain for prompt orchestration and containerized shells for safe code execution—core components Keystone is replicating and expanding upon.

These case studies validate the feasibility of autonomous coding agents at scale and illustrate the concrete performance gains that early adopters can expect.

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How to Demonstrate Impact in Your Application

1. Quantitative Metrics

• “Reduced average PR turnaround from 48 h to 12 h by automating boilerplate code generation.”
• “Saved $15k/month on cloud compute by implementing token‑level caching.”

2. User Testimonials

• Include short quotes from developers who used your agent tool and saw measurable productivity boosts.

3. Open‑Source Contributions

• Highlight merged pull requests to LangChain, AutoGPT‑API, or related repositories.

4. Prototype Showcase

• Attach a video walkthrough of an autonomous agent building a simple CRUD API from a natural‑language spec.

By aligning your experiences with Keystone’s mission, you position yourself as the perfect founding engineer to drive the autonomous coding agent platform forward.