Director, Platform Engineering

Bring more to life. At Danaher, our work saves lives. And each of us plays a part. Fueled by our culture of continuous improvement, we turn ideas into impact – innovating at the speed of life. Our 60,000+ associates work across the globe at more than 15 unique businesses within life sciences, diagnostics, and biotechnology.  Are you ready to accelerate your potential and make a real difference? At Danaher, you can build an incredible career at a leading science and technology company, where we’re committed to hiring and developing from within. You’ll thrive in a culture of belonging where you and your unique viewpoint matter. Learn about the Danaher Business System which makes everything possible. The Director, Platform Engineering role is responsible for end-to-end reliability of the platform behind our AI initiatives via accelerated compute, cloud infrastructure, DevOps and release engineering, application support, and incident response. Your customers are the teams building AI into consequential work across the company: molecular design, autonomous labs, supply chain, professional services, and more. You will keep their systems available and performant today, and define what reliability looks like as workloads become increasingly LLM-driven and agentic. This position reports to the Senior Director, Data and AI Platform, part of the Chief Information Officer (CIO) office and will be located onsite in Kraków, Poland. This is a Danaher Corporate role, hosted by our Cytiva operating company in Kraków. In this role, you will have the opportunity to: End-to-end platform reliability. Own availability, performance, and recovery for the compute and cloud platform underpinning the company's AI initiatives — you are accountable for whether the systems our AI teams depend on are working, not just for the components your team builds. Incident management and response. Lead the incident lifecycle across the platform: detection, triage, mitigation, and post-incident review. Establish SLOs, error budgets, and on-call practices that hold across a diverse set of workloads and drive measurable reduction in time-to-detect and time-to-mitigate. Compute and cloud infrastructure for AI workloads. Design and operate the GPU/accelerator fleet, scheduling and capacity management, storage, and networking that support large-scale training, inference, and simulation — balancing utilization, cost, and the reliability expectations of production-facing teams. Application support and operations. Own the operational layer between infrastructure and the applications running on it: deployment, configuration, runtime health, and the support model that gets AI teams unblocked quickly when something breaks. DevOps and delivery pipelines. Own CI/CD, infrastructure as code, environment management, and release engineering so that changes ship rapidly and safely, and so that reliability is enforced in the pipeline rather than discovered in production. Serving internal AI customer teams. Treat corporate-wide AI initiatives — molecular design, autonomous labs, supply chain, professional services, and others — as your customers. Gather their reliability and capacity requirements, translate friction into scoped platform work, and prove impact through before/after measurement. Building toward agentic systems. Set the reliability strategy for the next generation of LLM-based and agentic workloads: observability and evaluation for non-deterministic systems, guardrails and governance for agents acting in production, and the infrastructure patterns that make agentic execution safe, traceable, and dependable at scale. The essential requirements of this job include:

Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience; advanced degree welcome but not required. Functional experience: 10+ years in engineering, with significant time owning production reliability, SRE, or platform