Associate Director, Platform Engineering

About the role

As Associate Director, Platform Engineering you will lead the team responsible for the hybrid compute infrastructure, developer platform and scientific compute environments that power Relation’s machine learning and drug discovery work. Reporting to the VP of Engineering, you will collaborate closely with peers across Engineering, Data Science and Machine Learning to define and evolve the company’s platform strategy.

Key responsibilities

• Partner with the VP of Engineering and cross‑functional peers to shape Relation’s platform strategy.
• Own the architectural strategy for Kubernetes‑based clusters and the broader hybrid infrastructure, establishing technical standards and guardrails.
• Lead and develop the Platform Engineering team, hiring, mentoring and creating conditions for high performance.
• Own the scientific compute platform, ensuring reliable, efficient support for ML and data‑science workloads.
• Drive maturity of CI/CD pipelines, internal services and developer tooling, treating the platform as a product for internal engineers and scientists.
• Establish comprehensive observability, disaster‑recovery and failover strategies across on‑premises and cloud environments.
• Manage platform security controls, including IAM, secrets management, network policy and vulnerability management, to meet clinical‑stage biotech data‑governance standards.

Required profile

• Significant experience in platform, infrastructure or DevOps engineering with a proven record of building and operating complex production environments.
• Experience leading engineers, setting technical direction, mentoring teams and taking accountability for live production systems.
• Strong hands‑on operational experience, including on‑call rotations and incident response.
• Deep expertise in Kubernetes and hybrid on‑premises/cloud environments.
• Experience building or evolving MLOps infrastructure such as Kubeflow, GPU compute for distributed training and model serving.
• Familiarity with batch compute for data‑intensive scientific workloads and the reliability requirements of scientific pipelines.
• Track record of maturing CI/CD pipelines, developer tooling and observability across multi‑environment platforms.
• Strong security fundamentals with hands‑on experience designing and implementing IAM, secrets management and vulnerability controls.

Required skills

• Kubernetes
• Kubeflow
• MLOps
• CI/CD pipelines
• Observability
• IAM and secrets management
• Network policy and vulnerability management
• Hybrid cloud infrastructure
• GPU compute and distributed training
• Batch compute for scientific data pipelines