Director, Platform Engineering

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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:

Education: 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 operations for large-scale distributed systems, and demonstrated ownership of reliability outcomes at organizational scale — you can point to concrete before/after results in availability, MTTR, incident volume, or error-budget performance from programs you led.

Leadership experience: Proven success leading and scaling multidisciplinary engineering organizations, including platform tech leads and senior engineers across distributed time zones — recruiting, coaching, setting technical direction, and holding a team accountable for operational outcomes.

Deep, hands-on experience running incident management for business-critical systems: on-call design, escalation paths, blameless post-incident review, and the follow-through that turns findings into permanent fixes.

Production experience with major cloud platforms (AWS, Azure, or GCP), containerization and orchestration (Docker, Kubernetes), and infrastructure as code (Terraform / Pulumi / etc), operating infrastructure for ML/AI workloads at scale — accelerated compute, distributed training or high-throughput inference, job scheduling, and the associated capacity and cost management.

Strong observability expertise: metrics, logging, tracing, and SLO instrumentation, plus a track record of driving toil reduction and automation rather than adding headcount to absorb operational load.

Excellent written and verbal communication, and the ability to work with demanding internal customers — negotiating trade-offs, setting expectations, and representing platform reliability to senior leadership.

Travel, Motor Vehicle Record & Physical/Environment Requirements:

Ability to travel – up to 20%

It would be a plus if you also possess previous experience in:

Hands-on experience with LLM and agentic systems in production — inference serving, evaluation and guardrails, or observability for non-deterministic workloads.

Familiarity with scientific or research computing environments (HPC, lab automation, instrument data pipelines) or other domains where AI workloads sit close to physical systems.

Experience operating in compliance-constrained or isolated environments (SOC 2, FedRAMP, GxP, or similar), or across multiple clouds and on-premise infrastructure.

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Join our winning team today. Together, we’ll accelerate the real-life impact of tomorrow’s science and technology. We partner with customers across the globe to help them solve their most complex challenges, architecting solutions that bring the power of science to life.

For more information, visit www.danaher.com .