Member of Technical Staff

Member of Technical Staff

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 (NeoLink) 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 the 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 a Member of Technical Staff to own the systems behind our drug discovery research. Our small engineering team works across scientific data pipelines, model training and evaluation, inference, compute infrastructure, and developer tooling. You will own projects across these areas as priorities evolve, working with researchers to identify important problems and take solutions from investigation through deployment and ongoing operation.

Your work might involve integrating a dataset into training, improving GPU workload performance and reliability, building an API for scientific results, or automating an experimental workflow. You can navigate unfamiliar code, understand the scientific and technical constraints, and make decisions backed by evidence.

Our environment centers on Python and PyTorch, with Linux, containers, Kubernetes, cloud storage, and shared GPU compute. We value demonstrated technical depth, curiosity that leads you to investigate how things work, and the ability to become effective in unfamiliar parts of the stack.

Relevant areas of expertise might include 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

• Build reliable scientific workflows that colleagues can run, reproduce, and troubleshoot with less manual intervention.

• Integrate models, datasets, and evaluation methods while preserving scientific validity, data correctness, and compatibility with existing experiments.

• Improve the performance, cost, and reliability of shared compute through better scheduling, monitoring, failure recovery, and artifact management.

• Identify and remove bottlenecks in data preparation, execution, and evaluation so researchers can iterate faster.

• Build and maintain APIs, libraries, and automation that reduce manual work and accelerate research.

• Identify the engineering gaps that constrain research progress, set priorities with the team, and demonstrate the impact of the solutions you deliver.

Specific Skills and Qualifications

• Strong Python and software engineering fundamentals, including data structures, interface design, testing, concurrency, and systematic debugging.

• A track record of owning substantial software systems from design through deployment and ongoing operation, including diagnosing failures and improving performance and reliability.

• Experience building or supporting ML or scientific computing workflows, with an understanding of how data, model execution, and evaluation fit together.

• Practical PyTorch experience, including debugging model execution and understanding how data loading, device placement, gradients, and memory use affect training and inference.

• Experience running software on Linux, working with containers, and shipping maintainable changes through automated tests and CI/CD.

• Demonstrated ability to learn unfamiliar systems, make technical decisions independently, and communicate the reasoning and tradeoffs clearly.

• Ability to own problems across application code, data pipelines, ML execution, and infrastructure, and develop the depth needed to solve them.

• Ability to use AI development tools effectively and take responsibility for the correctness and maintainability of the resulting code.

• Experience with Kubernetes, cloud platforms, workflow orchestration, databases, or computational biology and chemistry is valuable. A biology or chemistry background is not required.