Join Genesis Molecular AI as an intern to work on high-impact generative AI research projects in a collaborative environment.
Posted by employer 6 days ago
First seen on Joblaze 5 days ago
Last verified on the company career page 5 days ago
Skills & Technologies
What you'll build
Must have
Nice to have
AI in the day-to-day
We are conducting fundamental research at the intersection of machine learning, physics, and computational chemistry.
Requirements
Not disclosed in this posting: compensation, years of experience, work arrangement, visa sponsorship.
Benefits
Joblaze summary
The ML Research Intern at Genesis Molecular AI will engage in hands-on research, focusing on building and testing generative AI models to address significant scientific challenges. Proficiency in coding and a strong foundation in machine learning are essential, particularly in areas like diffusion models and reinforcement learning. This role is ideal for students pursuing a Bachelor's or Master's degree in a technical field, eager to apply their skills in a collaborative research environment. The intern will benefit from dedicated mentorship and the opportunity to contribute to impactful projects within a leading team in AI and biochemistry.
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From the original posting
About the Team
Join a world-class team at the forefront of AI and biochemistry.
At Genesis Molecular AI, we’re a tight-knit team of proven deep learning researchers, software engineers, and drug discovery pioneers. Our shared mission is nothing short of revolutionary: to forge the next generation of AI foundation models that will unlock groundbreaking therapies for patients with severe diseases.
We don’t just apply machine learning to biology; we are conducting fundamental research at the intersection of machine learning, physics, and computational chemistry, pushing the boundaries of each field. You will work side-by-side with top multidisciplinary researchers to design and build generative foundation models at scale, having access to ample compute and large-scale simulations.
About the Role
This is an opportunity to operate as a full member of our research team for the duration of your internship. You will be paired with a mentor and get hands-on experience building and testing models at the forefront of generative AI. Your high-impact project will likely involve working with our foundation models and could touch on cutting-edge areas like diffusion models, large language models (LLMs), or reinforcement learning. We’re looking for exceptional students passionate about applying their technical skills to challenging research problems and contributing directly to our mission.
You Will:
Contribute to a high-impact research project by building models, running experiments, and analyzing results for a key scientific challenge in generative AI.
Turn research ideas into high-quality code, implementing and optimizing multi-modal models and algorithms from the latest literature.
Design and run experiments at scale to validate promising approaches and hypotheses.
Present your work and findings to the team, contributing to our collaborative research environment.
You Are:
Currently enrolled in a Bachelor's or Master's program in Computer Science, Machine Learning, or a related technical field.
A skilled and agile coder with a passion for writing clean, efficient, and reliable code.
A curious and tenacious problem-solver, excited to tackle complex technical challenges.
Eager to learn about the intersection of AI and biochemistry and the drug discovery process.
What we offer:
A high-impact project, not a toy problem. Your work is chosen to have a direct line of sight to advancing our core scientific platform.
Dedicated mentorship from a senior researcher or engineer on our team who will partner with you, guide your project, and champion your growth.
Deep immersion in a world-class team. You'll join our paper discussions, research talks, and social events, becoming a true member of the Genesis AI team. The team reads and discusses 1-2 ML or chemistry papers every week to stay on top of the field and inspire new ideas.
Past intern projects include:
Developing a post-training protocol to improve LLM reasoning for drug discovery tasks
Designing a more efficient co-folding model architecture
Building a vector embedding method to tractably search billions-scale molecule libraries
Optimizing reinforcement learning algorithms to improve state-of-the-art diffusion models
Inventing novel inference-time steering methods to enhance co-folding model performance in the hands of chemists
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