AI Scientist
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Before you apply
LocationZurich
Typefull-time
SenioritySenior
Experience4+ yrs
DepartmentAI Science & Machine Learning
EquityIncluded
Company size7+ people
PostedMar 18, 2024 · 2y ago
Verified live1d ago
At a glanceSummarised by Seekless from the posting.
MS/PhD degree in Computer Science, Statistics, Applied Mathematics, Computational Biology, Computational Chemistry or other related subject
Track record of contributing to novel methods for state-of-the-art deep 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
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
Will also consider BS degrees in these areas for candidates highly qualified across other requirements or with significant work experience
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
Skills
PyTorch
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
structure prediction
cofolding
curriculum learning
multi-task learning
transfer learning
Benefits
Competitive salaries
Equity
401(k) match
Medical insurance
Dental insurance
Vision insurance
PTO
Maternity leave
Paternity leave
In office lunch
About Proxima
Proxima (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 with our talented team of scientists and engineers we are uniquely well-positioned to discover and develop a new class of medicines. Come join us!
About You
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.
Key Responsibilities
•
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
Basic Requirements
•
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
Preferred Qualifications
•
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
Full-Time Employees at Proxima enjoy these Perks*:
•
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