AI Scientist

2 г. назад

SwitzerlandEuropeMiddle

machine learningprotein structure predictionmolecular generationmass spectrometry

AI Scientist role focused on advancing AI-native drug discovery using protein interaction modeling and molecular generation.

Обязанности

Scientifically direct the design and training of large-scale, state-of-the art deep learning systems
Develop novel architecture and training paradigms to lead the industry in unsolved scientific problems
Collaborate with content experts from other domains (e.g., chemistry, physics, biology) to enable innovative feature-engineering and novel cross-disciplinary approaches
Actively contribute to top-tier ML conferences and journals and attend core ML conferences to stay connected with the community and current trends

Требования

We are looking for talented AI Scientists to join our team to develop the world’s most advanced pipeline for the design of proximity-inducing molecules. You will conduct research at the bleeding edge of our field, and challenge the SOTA across protein structure prediction and molecular glue design. You will work with a team of world-class machine learning research scientists in an interdisciplinary, research-heavy position on a range of unsolved problems.
MS/PhD degree in Computer Science, Statistics, Applied Mathematics, Computational Biology, Computational Chemistry or other related subject (will also consider BS degrees in these areas for candidates highly qualified across other requirements or with significant work experience)
Track record of contributing to novel methods for state-of-the-art leep learning (in industry or through publications)
Expertise in ideally several of the following topics: diffusion models, flow matching, transfusion, discrete diffusion, latent diffusion, VAEs, image generation, video generation, LLMs, multimodal LLMs, pre-training, post-training, reinforcement learning, SFT, DPO/GRPO, conditioning, classifier(-free) guidance, LORA, constrained generation scaling, distributed training, tokenization, geometric deep learning, equivariant models, structure-based drug design (SBDD), structure prediction / cofolding, curriculum learning, multi-task learning, transfer learning
4+ years of ML research experience in industry or academia, with strong familiarity with PyTorch
Ability to understand business problems and how to build models that can quickly drive value, while prioritizing your research efforts accordingly

Будет плюсом

Strength across core ML fundamentals
Deep experience in core NLP/CV
Extensive hands-on experience and ability to design and tune models from scratch
Experience with large models and scaling
Experience with generative models for molecules/proteins

Условия

Highly competitive salaries
Company Equity Package, everyone is a stakeholder!
401(k) + Company Match
Medical, Dental, & Vision Insurance (PPO w/ HSA & FSA options)
13 Paid Holidays + Unlimited PTO & Sick Time
Maternity leave 18 Weeks of fully paid leave + 6 weeks for Paternity leave
In-Office Lunch (5 days per week)
*for US based FTEs, country specific benefits apply for locations outside US

Другое

(formerly VantAI) is advancing an AI-native approach to drug discovery by making protein interactions programmable. Our platform brings together foundation-model machine learning, a scalable data generation engine, and a partnership track record exceeding $5B in collaborations across the world’s leading biopharma and tech organizations. We’ve recently closed an oversubscribed seed round partnering us with an elite group of sophisticated and dedicated VCs including DCVC, Nvidia’s Nventures, AIX, Yosemite among others.
Neo-1 is our all-atom foundation model that combines state-of-the-art structure prediction and molecular generation in a single system. Neo-1 enables rapid exploration of chemical and structural space for high value, previously intractable targets, and in particular unlocks small molecule proximity therapeutics like molecular glues with AI for the first time.
In parallel, we are developing an advanced structural interactomics platform built on proprietary XLMS technology and a lab equipped with next-generation mass spectrometry instrumentation. This platform produces proteome-scale maps of protein interactions and helps identify small molecules that modulate proximity. Together with Neo-1, it creates an integrated system capable of co-folding protein complexes while generating candidate small molecules to influence those interactions. Proximity-based therapeutics represent one of most promising frontiers in modern drug discovery with the potential to treat previously intractable diseases and target ‘undruggable’ proteins. Our technology combines proteome-scale structural data with state-of-the-art generative AI foundation models, and coupled wi