Founding AI Engineer
US$200,000 – US$300,000 / yr base salary
Posted 14 Apr 2025
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 2+ yrs
- Management level
- Individual contributor
- Employment
- On-Site
- Contract
- Full-time
| Base salary | US$200,000 – US$300,000 / yr |
|---|
Field of studyComputer Science, Data Science, Engineering, Machine learning
Study levelMaster's or higher
At Falconer, we’re transforming how engineers create, access, and share knowledge with each other and their AI agents. We’re looking for a Founding AI Engineer to help us build an AI-powered knowledge platform that companies love.
As a founding engineer, you'll have a major impact on our product development, technical direction, and culture.
Key responsibilities:
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Participate in the entire product development lifecycle, including architecture, coding, testing, and deploying
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Research and apply best practices in terms of Knowledge Graph, embeddings, vector and graph RAG search, fine-tuning, and model optimization
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Develop and maintain backend systems including API services, databases, and server-side logic
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Optimize tech stack and applications for maximum precision and speed
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Write clean, efficient, and well-documented code, following industry best practices—the Falconer platform will help you!
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Stay updated with emerging technologies in web development and make recommendations for adopting new tools and practices
Who you are:
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Advanced degree in Machine Learning, Computer Science, Data Science, Engineering, or equivalent work experience
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Minimum of 2 years of professional experience in developing ML data pipelines
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Experience with text embeddings, RAG systems, and similarity search
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Familiar with Entity and Relationship recognition models
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Skilled at prompt engineering
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Ability to implement backend systems in Python and/or Node.js
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You can evaluate tradeoffs and propose the most appropriate storage solution (SQL, NoSQL, Vector DBs, Graph databases)
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You enjoy hard problems and open-ended assignments
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You use data to make decisions, and experiment your way around hard problems
Bonus points:
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You've developed products with LLMs before (extra if you've already built your own agents)
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You have fine-tuned foundational models to tailor responses to specific data use cases
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You have experience with LlamaIndex and Memgraph to produce knowledge graphs
Perks and benefits: