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Falconer

AI-powered engineering knowledge management platform

Founding AI Engineer

Full-Time
$200k - $300k/yr+ Equity package
Junior
Bachelor's
San Francisco, CA, USA
HybridFlexible arrangements may include in-office or hybrid work; the posting does not specify required office days.
Software EngineeringAI Application Engineering, Backend Engineering

About the job

Requirements
  • An advanced degree in Machine Learning, Computer Science, Data Science, or Engineering, or equivalent work experience.
  • At least 2 years of professional experience developing machine-learning data pipelines.
  • Experience with text embeddings, retrieval-augmented generation systems, and similarity search.
  • Familiarity with entity and relationship recognition models.
  • Prompt engineering skills.
  • Ability to implement backend systems in Python and/or Node.js.
  • Ability to evaluate tradeoffs and propose appropriate storage solutions, including SQL, NoSQL, vector databases, and graph databases.
Responsibilities
  • Participate in the entire product development lifecycle, including architecture, coding, testing, and deployment.
  • Research and apply best practices in knowledge graphs, embeddings, vector and graph retrieval-augmented generation search, fine-tuning, and model optimization.
  • Develop and maintain backend systems, including API services, databases, and server-side logic.
  • Optimize the technology stack and applications for precision and speed.
  • Write clean, efficient, and well-documented code, following industry best practices.
  • Stay updated with emerging technologies in web development and recommend new tools and practices.
Desired Qualifications
  • Prior experience developing products with large language models, especially building agents.
  • Experience fine-tuning foundational models to tailor responses to specific data use cases.
  • Experience with LlamaIndex and Memgraph to produce knowledge graphs.

About the company

Falconer.ai provides AI-powered knowledge management tools for engineers and technical professionals. Its platform offers a unified interface where users can generate prose, diagrams, code samples, and data tables, all powered by AI agents that organize and continuously update information to maintain accuracy. The product works by integrating multiple content creation and data-generation features in one workspace, with automation handling the curation and refreshing of knowledge. This differentiates Falconer.ai from competitors by focusing on automation and accuracy for engineering knowledge within a single platform, rather than offering loosely connected tools. The company’s goal is to improve the reliability and efficiency of engineering work by keeping knowledge up to date and easy to access through a subscription-based model.

Company Size

11-50

Company Stage

N/A

Total Funding

N/A

Headquarters

San Francisco, California

Founded

2024

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Simplify's Take

What believers are saying

  • Falconer reported GA launch in February 2026 and active changelog momentum.
  • Customer stories from Vori and Payabli show concrete documentation replacement wins.
  • SOC 2 Type II and self-hosted options unlock regulated enterprise deals.

What critics are saying

  • Notion, Glean, and Confluence already own enterprise knowledge workflows and budgets.
  • Its differentiation depends on MCP adoption and agent workflows; platform shifts can commoditize it.
  • If auto-updates miswrite runbooks or specs, engineering trust collapses and churn spikes.

What makes Falconer unique

  • Falconer launched publicly in February 2026 with AI-native knowledge maintenance.
  • It auto-updates docs from GitHub, Slack, Linear, and Drive changes.
  • Read-write MCP integrations feed verified context into Cursor, Claude Code, and Slack.

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Benefits

  • 401(k) Retirement Plan
  • Flexible Work Hours
  • Hybrid Work Options
  • Company Equity

Growth & Insights

6 month growth

8%

1 year growth

0%

2 year growth

0%