ML Infrastructure Engineer

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 engineers with an insatiable hunger for solving bleeding-edge scientific problems spanning biology, chemistry, physics, data science and computational science domains to join our core application team.

Successful candidates will develop large-scale scientific workflows, provide support to our scientists on existing workflows and proactively identify areas for increasing automation to reduce inefficiencies. Projects may include developing cheminformatics tools and algorithms; large-scale virtual screening and protein-protein docking; creating scalable on-demand ML inference infrastructure; developing scalable chemical databases; automation of modeling, analysis, and visualization of simulations; and a range of similar tasks integral to achieving our mission of becoming the leading AI-enabled drug discovery in the field of induced proximity.

Our tech stack spans several languages and frameworks, including but not limited to: Python, Go, Rust, React, running in Docker/Kubernetes on GCP. Relevant areas of expertise might include workflow engineering, tool integration, high performance cloud computing, systems engineering, and machine learning, but specific knowledge of any of these areas is less critical than versatility and a willingness to learn and work anywhere in the tech stack. 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.

Outcomes for this Role:

• Shaping our product by spearheading new features and services

• Developing software tools to automate or improve processes ranging from data ingestion pipelines to bespoke cheminformatics workflows

• Contributing to the culture of a rapidly growing company

• Being challenged by your colleagues and learning something new every day

Specific Skills and Qualifications:

• Experience working in complex codebases and cloud-native architectures

• Container orchestration and Kubernetes cluster development

• Demonstrated experience creating and managing infrastructure for ML model registry, training, inference and deployment infrastructure

• Strong familiarity with best practices and current state of the art for ML infrastructure

• Experience supporting large-scale model architectures with complex environment requirements

• Data architecture and engineering

• Demonstrated experience with parallel computing and distributed model architectures

• Ability to break apart existing code; solve ad hoc problems with little help

• Willingness to work with existing codebases

• No task too big or small; strong sense of independence and ownership

• Provide guidance and opinions on implementation/considerations, while having a willingness to compromise

• Developing/supporting ML models in the cheminformatics, bioinformatics, large language, and/or diffusion model domains (desired)

• Experience working on drug discovery projects (desired)