ML Engineer

Machine Learning 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, and 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 versatile software engineer to build the tools and systems behind our drug discovery research. You will work across automation, infrastructure, data, and ML, helping scientists develop models, run experiments, and use the results.

You enjoy taking a problem from investigation through deployment. Your work might involve integrating a dataset into training, diagnosing a stalled inference job, building an API for scientific results, or automating a manual workflow. You can find your way around an unfamiliar codebase, understand the constraints, and deliver a focused, well-tested improvement.

Our environment centers on Python and PyTorch, with containerized workloads, Kubernetes, cloud storage, and shared GPU compute. You will work closely with researchers and infrastructure engineers.

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 new models, datasets, and evaluation methods while preserving data correctness and compatibility with existing experiments.
• Improve compute workflows through better job submission, monitoring, failure recovery, and artifact tracking.
• Identify and remove bottlenecks in data preparation, execution, and evaluation so researchers can iterate faster.
• Deliver libraries, services, and developer tools that are easy to use and maintain, with appropriate tests, documentation, and monitoring.
• Contributing to the culture of a rapidly growing company
• Being challenged by your colleagues and learning something new every day

Specific Skills and Qualifications

• Strong Python and software engineering fundamentals, including data structures, interface design, testing, concurrency, and systematic debugging.
• A track record of delivering software that other people use and depend on, including maintaining and troubleshooting it after release.
• Experience building or supporting ML or scientific computing workflows, with an understanding of how data, model execution, and evaluation fit together. PyTorch experience is a must.
• Experience running software on Linux, working with containers, and shipping changes through automated tests and CI/CD.
• Ability to break down ambiguous problems, make progress independently, and explain technical tradeoffs with evidence.
• Willingness to work across automation, infrastructure, data, and ML, with a strong sense of ownership and clear communication with scientists and engineers.
• 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. We welcome strong engineers from other technical domains; a biology or chemistry background is not required.