Computational Biologist

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!

Proxima is seeking highly-motivated Computational Biologists interested in building the leading AI-enabled drug discovery platform for novel induced proximity therapeutic modality.

Successful candidates will work closely with the Computational Chemistry, Machine Learning and Application Engineering teams to tap into the wealth of evolutionary, structural and -omics data to unlock the full potential of unified machine learning representations and large-scale geometric deep learning generative models.

Projects may include designing large-scale scientific workflows for mining public and unique in-house data; developing novel systems biology and structural biology computational methods, tools and algorithms; and/or designing, training and fine-tuning best-in-class machine learning models. In our projects we apply induced proximity approaches to some of the most challenging targets considered “undruggable” as well as discover, build and develop novel proximity platforms spanning across multiple targets and disease areas.

We value individuals who want to make an impact, have a deep intellectual curiosity, enjoy solving challenging problems and have a track record of achievement. At Proxima, we believe that success is determined by talent and hard work, and individuals are given equal opportunities to grow and advance based on their abilities and contributions.

• Develop pilot capabilities and create large-scale data pipelines from the ground up 

• Collaborate with other teams across Proxima and partnering organizations to achieve project goals by contributing code, analyses, benchmarks, optimizations and ideas to ongoing research-driven projects

• Integrate biological insights into the next generation of AI/ML models for drug discovery

• Validate and interpret computational predictions by working closely with experimentalists 

• Hold an MSc or PhD degree in computational biology, bioinformatics or a related field

• Possess mixed academic and/or industrial experience, preferably in the biotech industry

• Have a strong background in computational structural or systems biology

• Demonstrate strong programming skills in Python, R and/or other languages

• Experience building reproducible and scalable computational biology workflows

• Experience designing and applying machine learning models

• Experience with geometric deep learning, graph neural networks, protein language models, transformers and representation learning is a plus

• Preferably have prior exposure to drug discovery problems

• Experience working with cloud-based infrastructure

• Agile project management

• 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