About Us
At Dyneti, we believe digital payments should be seamless and secure. That’s why we built DyScan, a software library that allows digital merchants to prevent fraud and improve conversion by taking a photo of a credit card.
Dyneti was founded by a fraud prevention expert from Uber, and has raised funding from an exceptional lineup of investors, including Y Combinator. We’ve processed hundreds of millions of credit card scans around the world, and our customers include Fortune 100 companies and some of the fastest growing tech unicorns.
Job Overview
Join Dyneti as part of the founding engineering team. You will have the opportunity to work closely with the CEO to build new features and roll out new products - all in the deep learning space.
We’re Looking for Someone Who
Enjoys significant ownership over user-facing product
Can work independently
Is comfortable turning research papers into product
Has 2+ years experience with deep learning or has a PhD in a STEM field
Builds with a product-first approach. You ship code quickly and care about the real world impact of your code.
Python expertise strongly preferred - however, candidates with deep learning experience in a different language will be considered
Familiarity with Tensorflow preferred
In the News
https://www.builtinsf.com/2020/1/21/50-san-francisco-startups-watch-2020
https://www.bizjournals.com/sanfrancisco/news/2021/07/23/0723-wwl-youngguns.html
Dyneti is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.
Develop machine learning models for autonomous vehicle prediction and planning, turning real-world driving challenges into robust systems that support safe, smooth behavior and integrate into production software.
Build and deploy machine learning systems for complex engineering challenges, partnering with data scientists, simulation engineers, and customers to turn research into reliable pipelines, tools, and products.
Build and scale Tower’s machine learning research platform, enabling reproducible experimentation, GPU-accelerated training, simulation, and efficient compute across HPC and cloud environments.
Optimize deep learning inference pipelines for microsecond-level latency, evaluating heterogeneous hardware, developing GPU kernels, and improving model efficiency, reliability, and deployment performance.
Deploy AI products into production by guiding customers through onboarding, prompting, evaluation, and fine-tuning, while partnering with research and product teams to solve complex LLM challenges.
