You have the opportunity to:- Join Onton as a Founding Engineer and set the strategic 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.
We expect you to:- Have 8+ 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.
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.
Onsite InterviewsOur final round of interviews is conducted onsite at our SF office, and this role requires full-time, in-person collaboration. We believe that the best work happens when our team is together, fostering creativity, spontaneous collaboration, and strong relationships.
If you're unable to attend the interviews onsite or work onsite full-time, this position may not be the right fit. We want to ensure all candidates have a clear understanding of our work environment before applying, so everyone's time is respected throughout the process.
LocationOur team works in hybrid, 3 days a week, in our new office in San Francisco (Jackson Square). We're offering relocation compensation as we build density in this area.
We have a tenure system where, after a year, any team member can transition to a fully remote, provided they operate primarily on Pacific Time.
