Secure Data Infrastructure for AI

Location: Primarily based in the Berlin or Palo Alto offices; availability to work with teams across the US and Europe, with meetings from 8am PT to 7pm CET. Travel several times a year for retreats and business events is expected.

Salary: $200K–$275K plus bonus and equity.

Company

hirify.global develops biological software and AI-driven tools for the rational design of medicines and biotechnologies.

What you will do

• Architect, implement, and own secure data infrastructure for AI model training and deployment, from data ingestion through model-weight storage and access.
• Build authentication, access-broker, secrets-management, key-management, and ingress/egress control services across a multi-cloud environment.
• Design and enforce RBAC/ABAC, audit logging, encryption, workload identity, and data lifecycle governance.
• Embed security controls into MLOps pipelines, including CI/CD, container, Kubernetes, namespace, and pod security.
• Conduct threat modeling and secure design reviews, identify vulnerabilities, and drive remediation across data systems, cloud environments, and ML tooling.
• Build detection and alerting for anomalous data access and exfiltration, while educating teams on secure coding and sensitive data handling.

Requirements

• 7+ years of hands-on experience in data engineering, infrastructure security, or software security.
• Production-quality programming skills in Python, Bash, and Go, Rust, or C++.
• Deep experience securing GCP environments, including IAM, VPC design, secrets management, workload authentication, and cloud security posture management.
• Experience designing identity and access management systems with credential issuance, rotation, and least-privilege enforcement.
• Hands-on Kubernetes security experience, including RBAC, namespace isolation, workload identity, and pod security.
• Ability to work across US and European time zones and primarily from the Berlin or Palo Alto office.

Nice to have

• Experience securing ML infrastructure, model registries, training clusters, dataset versioning, experiment tracking, and GPU environments.
• Terraform, GitOps security, automated misconfiguration detection, and remediation experience.
• Knowledge of AI/ML security risks, offensive security, threat modeling, penetration testing, cryptography, and secure enclaves.
• Experience with SOC 2, HIPAA, or GDPR controls and sensitive research data.
• CISSP, OSCP, GWAPT, or AI-focused security certifications.

Culture & Benefits

• Antedisciplinary environment combining molecular biology, machine learning, and software engineering.
• 30 days of paid vacation per year.
• Health insurance for US-based employees; 401(k) with company match in the US and Direktversicherung in Germany.
• Quarterly company-wide retreats and a monthly wellness benefit.
• Budget for office visits, conferences, courses, and professional development.
• Buddy support for onboarding.