Job Details

Our mission at Onton is to help people make decisions they love, instantly. We're tackling the most economically impactful decisions first: the average shopping journey takes 79 days, and we're taking that to less than 1.

Since our public launch in late 2023, we've grown exponentially to guide millions of users to their perfect product. We recently built the foundation of a neurosymbolic AI agent that learns more about the world on every search. You have the chance to solve equally difficult problems as you scale Onton 10x over the next few months to serve 1B+ searches/year, which is just the beginning.

You have the opportunity to:

• Design, scope, implement, and deploy user-critical features.
• Work with a fast-paced 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.
• Have 3+ years of relevant engineering experience, preferably in fast-scaling companies.
• Be a self-sufficient 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.

Interesting problems we've worked on recently:

• 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.

We'd love it if you:

• 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.

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