Senior Staff Software Engineer

As a senior technical leader on the Platform team, you will shape the architecture and evolution of cloud-based infrastructure that empowers scientific, data, and engineering teams to work more effectively. You’ll combine strategic technical direction with hands-on engineering, building scalable services across data platforms, distributed compute, and self-service research environments. Working closely with specialist teams, you’ll turn complex needs into reusable solutions while improving reliability, performance, and cost efficiency. You’ll also champion practical AI adoption, helping teams develop secure, cost-effective agentic workflows and LLM-powered applications. This role offers the opportunity to influence engineering practices across the organization, mentor other engineers, and contribute to technology supporting earlier cancer detection. The position is remote within the United States, with occasional travel to the headquarters in Brisbane, California, approximately two to three times per year.

• 12+ years of relevant software engineering experience, or equivalent, ideally in platform engineering, delivering complex systems from design through production.

• At least 3 years leading complex, cross-team initiatives, aligning stakeholders, communicating technical tradeoffs, and enabling delivery without relying on formal authority.

• At least 3 years building and operating production platforms using Infrastructure as Code on a public cloud, preferably Azure or Google Cloud Platform (GCP).

• Strong Python proficiency and experience with at least one additional high-level programming language.

• Deep understanding of Linux internals, networking, and virtualization, with hands-on system-level troubleshooting and performance tuning experience.

• Strong Kubernetes expertise, including ownership of advanced capabilities such as controllers, operators, custom resource definitions (CRDs), admission control, scheduling, autoscaling, service mesh, or multi-cluster networking.

• Sound judgment in distributed systems and platform architecture, including governance, capacity planning, reliability, cost management, data processing, and storage.

• Demonstrated software architecture leadership, including writing clean production code, clear design documentation and architecture decision records (ADRs), and facilitating constructive technical reviews.

• A track record of helping engineers and domain experts grow, strengthening teams’ capabilities and independence.

• Experience partnering with specialist teams such as data science, AI/ML, computational biology, or related technical disciplines to improve their workflows.

• Hands-on experience with AI-assisted development and building reusable tools or workflow automation.

• Preferred additional experience or interest in life-sciences software, next-generation sequencing (NGS) pipelines, biological data, MLOps, machine learning, deep learning, and scientific Python frameworks.

• Familiarity with workflow orchestration, distributed compute on Kubernetes, cloud migrations, multi-cloud or multi-region operations, columnar processing, open lakehouse technologies such as Apache Iceberg, and FinOps practices is an advantage.