Ask HN: Who wants to be hired? (August 2024)

Location: Fargo ND

Remote: no preference

Willing to relocate: yes

Technologies: ASP.NET C#, SQL, Vue.js, Dapper, HTML CSS JS.

Résumé/CV: https://github.com/denys-olleik/accounting

Email: dolleik@gmail.com

I've created a framework that implements double-entry accounting method within a relational database. It's a half-finished web application that allows for greater control over final implementation. So you can create accounting and ERP systems for anything from a laundromat to an aircraft carrier.

Location: United Arab Emirates or Remote Globally

Remote: Yes, or on-site

Willing to relocate: Yes, in United Arab Emirates

Technologies: Machine Learning, Python, Rust

GitHub: https://github.com/ahmrz>

Résumé/CV: https://ahmrz.gitlab.io/portfolio>

Email: ahmrz [at] outlook [dot] com

I am a recent graduate with master’s in artificial intelligence. Looking for entry-level machine learning / data science opportunities in UAE or remote globally.

  Location: NYC
  Remote: Either on-site or remote is fine
  Willing to relocate: No
  Technologies: Machine learning, generative AI, text-to-image models, JAX, Python, Rust, PyTorch, OCaml, Haskell
  Résumé/CV: https://www.echonolan.net/resume/cv.html (HTML) or https://www.echonolan.net/resume/cv.pdf (PDF)
  Email: echo@echonolan.net

Ideally, I'd find a job doing ML engineering on text-to-image, text-to-video, text-to-audio or related models - recently I've built a text-to-image model that is trained with unlabeled images alone, using CLIP for the link between captions and images[1]. I'm interested in ML engineering in other domains as well, and my last job was building a new cryptocurrency, so I have skills there too.

Here's my blurb about the model I built:

Recently, I've built a text-to-image model that is trained without any text labels, using unlabeled images and CLIP for the link between captions and images. This has never been done or even investigated before. Results are promising so far. I think this work is the best representation of what I'm capable of. In the process, I gathered a dataset of 33 million images for training data, including removing redundant images, deduplicating, and taking stills from any videos. I ported a VQGAN implementation from PyTorch to JAX, built an efficient preprocessing pipeline, built transformer models in JAX, and designed and trained baseline models and more sophisticated ones. To support the approach I eventually settled on, I designed and implemented an efficient algorithm to sample unit vectors from a finite set, conditioned on the vectors being inside a spherical cap. For that I needed to write a Python library in Rust to help with constructing the space partitioning data structure used for sampling. The sampling algorithm gets used to generate training examples and the model learns to sample images conditioned on the image's CLIP embedding being within an arbitrary spherical cap.

[1]: https://www.echonolan.net/posts/2024-03-09-is-it-possible-to...