Staff Software Engineer

Requirements

• 10+ years of experience in software engineering with demonstrated impact at the staff level or equivalent
• Strong experience building and operating production systems at scale
• Deep expertise in distributed systems or compute-heavy environments
• Strong hands‑on experience in system architecture and design
• Fluent in Python and across the AWS / Postgres / SnowFlake / orchestration stack; comfortable on both sides of the code/infra boundary
• Serious hands‑on experience with LLM‑assisted engineering and spec‑driven development – you’ve gone past tooling and built workflow and team practice around it
• Strong ownership mindset with the ability to operate across teams and communicate effectively
• (Desirable) Experience with large‑scale compute platforms or infrastructure
• (Desirable) Exposure to ML infrastructure or model execution environments
• (Desirable) Experience with PyTorch, CUDA, or running custom models in production
• (Desirable) Experience designing or operating workflow orchestration systems
• (Desirable) Track record of optimizing systems for cost‑efficiency at scale

What the job involves

• We are hiring a Staff Software Engineer to join the Compute team to own and scale the systems that power Aqemia’s scientific platform
• As our research grows in complexity and scale, this role exists to ensure our compute infrastructure, workflows, and developer tooling enable – not limit – scientific progress
• You will bring strong software engineering rigor into a highly scientific environment, working closely with physicists, AI researchers, and engineers to industrialize workflows and improve reliability at scale
• This role offers a unique opportunity to shape the foundations of how cutting‑edge science is executed in production
• Success in this role means scientists can run complex models and workflows faster, more reliably, and at greater scale, with infra and tooling that empower, not limit them
• Design, build, and operate scalable compute systems supporting training, inference, and scientific workloads
• Improve the reliability, robustness, and performance of production systems used across research and platform teams
• Develop internal tooling to streamline model and workflow lifecycle (build, test, deploy, run)
• Drive architectural decisions across compute and data systems in collaboration with engineering and research teams
• Improve developer experience for scientists by reducing friction in running and scaling experiments
• Establish and promote best practices in system design, observability, and AI‑assisted / spec‑driven development
• Contribute to raising the engineering bar across teams through mentorship and technical leadership