I'm an ML engineer/ML research engineer looking for roles where there are gradients to descend, models to build, and/or datasets to gather. In general I want to advance the frontier of what's possible. My credentials are more towards research engineering than applied work, but I'm open to both. My last job was as a contract research engineer doing AI safety work at Redwood Research. I spent about half my time on our synthetic datasets of safe & unsafe LLM behavior, and the other half on fancy finetuning. I finetuned an LLM to exhibit unsafe behavior while fooling logistic probes trained to detect it, but only in deployment. Weird multi-part composite loss. My other big project is independent research in text-to-image models that are trained solely on unlabeled image data, exploiting CLIP embeddings for the link between text and images. Currently fighting with spherical flow matching models and racing the ICLR paper deadline.
If you need help getting ML systems up and running, getting them fast and correct, or if any of that stuff sounds interesting to you, get in touch.
I'm an ML engineer/ML research engineer looking for roles where there are gradients to descend, models to build, and/or datasets to gather. In general I want to advance the frontier of what's possible. My credentials are more towards research engineering than applied work, but I'm open to both. My last job was as a contract research engineer doing AI safety work at Redwood Research. I spent about half my time on our synthetic datasets of safe & unsafe LLM behavior, and the other half on fancy finetuning. I finetuned an LLM to exhibit unsafe behavior while fooling logistic probes trained to detect it, but only in deployment. Weird multi-part composite loss. My other big project is independent research in text-to-image models that are trained solely on unlabeled image data, exploiting CLIP embeddings for the link between text and images. If nothing goes horribly wrong I'll be submitting a paper for ICLR in about 7 weeks.
If you need help getting ML systems up and running, getting them fast and correct, or if any of that stuff sounds interesting to you, get in touch.
I'm an ML engineer/research engineer looking for work in that vein. I can train models, I can design architectures, I can implement papers, I can gather real and synthetic data, and I can in general solve the problems that come up when trying to build ML systems. Background: I recently ended a 3 month contract at Redwood Research doing AI safety research where I spent about half my time improving our synthetic data pipelines and about half finetuning LLMs to exhibit unsafe behavior in deployment but not training, and to do so while defeating probes trained to detect the unsafe behavior (but only in deployment). I also have an ongoing solo research project working on text-to-image models trained without any text, relying on embeddings for conditioning. [1] is a (very out of date) blog post about that. There'll be a paper soon inshallah. Before the ML stuff I worked at a startup where we built a new cryptocurrency, and before that another startup.
I'm looking for an ML engineering gig. I can help you gather data, preprocess it, design models, train models, etc etc. My ideal job would be doing generative AI stuff with images/audio/video, but I'm open to anywhere there's gradients to descend. Recently I've been working on a project building a text-to-image model that learns solely with unlabeled image data, relying on CLIP for the link between captions and images[1]. I think it's a) cool and b) demonstrative of strong abilities. At a higher level of abstraction you can think of this as embedding guided content synthesis. The model learns to generate images conditioned on their CLIP embedding being within an input spherical cap. If you center the cap on the CLIP embedding of some text you get images that look like they'd have that caption, if you center it on the embedding of another image you get semantically similar images. The radius of the cap determines how similar the outputs are.
Remote: Yes, but prefer in-office
Willing to relocate: Yes, to the Bay Area
Technologies: machine learning, transformers, LLMs, finetuning, flow matching, JAX, PyTorch, Python
Resume: https://www.echonolan.net/resume/cv.pdf
Email: [email protected]
I'm an ML engineer/ML research engineer looking for roles where there are gradients to descend, models to build, and/or datasets to gather. In general I want to advance the frontier of what's possible. My credentials are more towards research engineering than applied work, but I'm open to both. My last job was as a contract research engineer doing AI safety work at Redwood Research. I spent about half my time on our synthetic datasets of safe & unsafe LLM behavior, and the other half on fancy finetuning. I finetuned an LLM to exhibit unsafe behavior while fooling logistic probes trained to detect it, but only in deployment. Weird multi-part composite loss. My other big project is independent research in text-to-image models that are trained solely on unlabeled image data, exploiting CLIP embeddings for the link between text and images. Currently fighting with spherical flow matching models and racing the ICLR paper deadline.
If you need help getting ML systems up and running, getting them fast and correct, or if any of that stuff sounds interesting to you, get in touch.