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# Software Engineer

- **Company:** Onton Cutler
- **Location:** San Francisco, CA, United States (Remote available)
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Graph Database, Machine Learning, Object Detection, Web Applications, Search Engines, Functional Programming
- **Published:** September 4, 2026
- **Apply:** https://startup.jobs/software-engineer-onton-8134289

## About the Role

* Have 3+ years of relevant engineering experience, preferably in fast-scaling companies.
 * Be a self-directed generalist, capable of picking up any engineering problem and solving it, no matter how tedious.
 * Quickly and iteratively ship code that is correct, robust, scalable, and maintainable. We love elegant code, but we're not optimizing for that. We take pride in the excellence of the product we build.
 * Exercise exceptional communication and interpersonal skills., * Enjoy giving high-level abstractions low-level performance.
 * Have functional programming experience. If not, no problem; motivated engineers learn it quickly.
 * Have worked with NLP and ML models before and understand their capabilities and limitations.

## Description

* Join Onton as an founding Engineer and set the foundation for our Engineering Team
 * Design, scope, implement, and deploy user-critical features.
 * Work with a dynamic team of other engineers, AI researchers, and product designers tackling cutting-edge problems to create a seamless, cohesive experience for users.
 * Build across every layer of the stack - web app, container, infrastructure, etc.
 * Stay up-to-date on the latest technologies and techniques to continually enhance our system, yourself, and the rest of the team., * Creating our own proprietary graph engine from scratch, which ended up being 700x faster on natural language queries than RedisGraph (a SOTA graph database which outperforms competitors by up to 15,000x on some benchmarks).
 * Enabling a user to combine text and image queries - for instance, they could upload an image of a blue couch and say "like this, but in green". To do this, we made our neurosymbolic search engine multimodal.
 * Automatically making logical sense of a subset of natural language via statistical and symbolic parsing techniques.
 * Fine-tuning an ensemble of ML models to perform object detection on diffusion-model-generated images.
 * Building out a performant and scalable inference engine to support more powerful and nuanced natural language queries from our users.


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