[Hiring] ML Engineer @Proxima

Role Description

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
  • 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.

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.

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.