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About the Role
Nordran is seeking a Head of R&D to maximize system performance and harden their sensing platform for hyperscale deployment, accelerating AI infrastructure by making liquid cooling chemistry measurable and controllable.
Responsibilities
- Deliver commercial-grade robustness and performance.
- Push the current sensing architecture to its performance limits.
- Reduce variability, drift, and cross-sensitivity to defined target margins.
- Tighten repeatability and stability across solvent, temperature, and chemistry variation.
- Design and execute rigorous stress, aging, and reliability testing.
- Continuously identify and eliminate dominant performance bottlenecks.
- Translate experimental findings into concrete product improvements.
- Lead a small applied science team executing focused experimental programs.
- Ensure the system meets the reliability and stability standards required for large-scale AI data center deployment.
Requirements
- PhD in Physics, Physical Chemistry, Materials Science, or related field.
- Strong hands-on experimental track record in coupled physical–chemical systems.
- Relentless focus on tightening tolerances and reducing noise.
- Ability to think in governing variables, failure modes, and uncertainty bounds.
- Able to connect experimental rigor to real-world product requirements.
- Clear communicator with high ownership.
Location
- Berlin
Work Type
- Full-time
Experience Level
- Head of R&D
Education Level
- PhD
Benefits
- Competitive compensation with significant stock options.
- High ownership role in a fast-moving deep-tech startup.
- Work directly on hardware deployed with leading AI infrastructure customers.
- Modern lab and office in Berlin with the tools, budget, and autonomy to build fast.
- Compassionate, low-politics culture with high ambition and high ownership.
About the Company
- Nordran accelerates AI infrastructure by making liquid cooling chemistry measurable and controllable.
- Our patent-pending chemical–physical sensing technology continuously monitors coolant stability in hyperscale AI data centers to detect degradation before it becomes downtime.
- With pilot systems being deployed, we are now driving performance, robustness, and long-term stability to full commercial readiness.