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Carlos Gomez Uribe
Machine Learning, Data Science and Product Leader
Carlos Gomez Uribe is currently the Vice President of Machine Learning at Solugen, a company focused on sustainable chemical manufacturing.12 He joined Solugen in this role in 2024, bringing his extensive experience in artificial intelligence and machine learning to the field of biotech.2
Career Background
Before joining Solugen, Carlos had an impressive career in the tech industry:
- He worked at several major tech companies, including Google, Netflix, Facebook, and Apple.2
- At Netflix, he spent seven years, with the last four and a half years as Vice President of Product Innovation, where he was in charge of the recommendation algorithm.2
Education and Expertise
Carlos has a strong educational background:
- He holds degrees from top institutions like Stanford University and Massachusetts Institute of Technology.1
- His areas of study include Mathematics, Electrical and Computer Engineering, Statistics, and Medical Engineering.3
- He completed a PhD in systems biology at MIT.2
Role at Solugen
At Solugen, Carlos is applying his expertise in computational thinking and machine learning to:
- Accelerate the discovery and development of more sustainable chemical processes.3
- Focus on two main areas:
- Protein design
- Optimization of chemical processes2
His work aims to drive down costs, speed up progress, and ultimately contribute to Solugen's mission of decarbonizing the chemicals industry.2
Personal Motivation
Carlos is described as mission-driven, having moved between companies to work on causes he cares about, such as improving the quality of information, health, and now climate change.2 He was drawn to Solugen because of its potential to make a significant impact on sustainability and its fast-paced, startup environment.2
Highlights
🚀 Join Our Green Chemicals Revolution at Solugen! 🌍
We’re on an exhilarating mission at Solugen: decarbonizing the chemicals industry by reinventing chemical processes! Bring your machine learning, computational and science chops the the party, helping our teams of Biologists and Chemical Engineers develop the chemical manufacturing processes we need for a sustainable future and deploy them at scale. You will do this by helping us evolve genes for new proteins that act as enzymes, evolve organisms that produce our enzymes, synthesize better inorganic catalysts, and optimize the newly enabled reaction pathways from the bench to the manufacturing plant.
Join my data science and machine learning team: 🔍 1. Data Scientist (Chemical/Biochemical Optimization)
- Develop and deploy Bayesian optimization and related methods, and play a pivotal role in refining our cutting-edge chemical and biochemical processes, allowing us to scale them from labs to large scale manufacturing.
- More here: https://lnkd.in/gMZHB4ik
🔍 2. Machine Learning Research Engineer (Enzyme Design & Directed Evolution)
- Apply deep learning techniques, including diffusion and large language models, to the space of amino acid sequences.
- Take our directed evolution lab platform to the next level by designing the enzymes that enable the sustainable chemicals manufacturing of the future through ML. Your computational designs will be tested in the lab, and when successful, taken to the manufacturing plant.
- More here: https://lnkd.in/g-JVGWPz
Let's shape a greener future together. Apply now, and get in touch! 🌱🔬💡 Learn more about Solugen here: https://solugen.com/
#Solugen #GreenTech #ML4Good #SustainableChemistry #JobAlert Know anyone who might be interested? I’m #hiring.
Earlier this year at an internal Apple ML conference, I saw Arwen Bradley talk about how increasing randomness in the learning algorithm seemed to improve generalization in deep learning models. I found this observation puzzling, and was also interested in her approach, which included some of the stochastic processes math I had learnt and used for my PhD for a completely different field (Quantitative Biology). This led to a really fun collaboration exploring the behavior by borrowing some tools and ideas from statistical mechanics. Check out our first draft if interested, and please send us feedback and useful pointers. https://lnkd.in/gE7YyiZt #machinelearning #deeplearning
