Global Head of Infrastructure & Security

Albert helps chemists change the world by building technology that understands how they work. As chemists ourselves, we know whatit’slike when softwaredoesn’twork the way you do — stuck with systems built by people who never spent a dayatthe bench.

That’swhy we created Albert OS, the first AI-native operating system for materials science, designed to remove every obstacle between curiosity and discovery.

We're looking for a Head of Infrastructure & Security to build and own the global infrastructure, reliability, and security function s is a program-building role, not a program-inheriting one. You'll sit at the intersection of cloud architecture, site reliability engineering, and enterprise security, reporting directly to the VP of Engineering.

If you believe infrastructure done right is a competitive advantage, we want to hear from you.

• Cloud architecture across all engineering teams: common IaC tooling, unified monitoring, CI/CD standardization, and the internal developer platform.
• Albert's security posture end-to-end: patching, SAST/DAST, WAF, SIEM, EDR, IDS/IPS, data loss prevention, and Zero Trust across cloud and identity. You'll also own the third-party security partnership, enterprise audit and questionnaire response, and security awareness across the company.
• Cloud cost strategy in partnership with Finance: per-unit metrics, team-level budget visibility, vendor contracts, and leadership-facing dashboards on infrastructure efficiency.
• The global SRE function on a follow-the-sun model: an existing India team, US presence, and a planned Germany-anchored EU team.
• The ML/AI infrastructure platform that the data science team builds on.
• Interoperability with partner Azure environments: identity federation, private connectivity, and bring-your-own-key.

• An SLO framework, error budget model, and service tiering classification that give engineering and leadership a shared language for reliability trade-offs.
• A functioning incident program: escalation paths defined, on-call rotations in place, DR and business continuity strategy documented, and the first post-mortem run end-to-end.
• An SRE organization with clear regional ownership and a roadmap for the EU buildout.
• MLOps infrastructure ownership off the AI/ML team, so the data science team ships models without managing backend infrastructure.
• Our first enterprise security audit passed with an owned, defensible posture rather than a manually assembled response.
• A unified cloud architecture with measurable cost reduction against a defined per-unit baseline.

• 8+ years in cloud infrastructure, platform engineering, or site reliability engineering, with at least 3 years in a senior leadership role owning a team or function.
• Deep, current AWS expertise: ECS, multi-account organization design, IAM and KMS, Cloud Trail-based audit evidence.
• Container orchestration at architectural depth, decision-making rather than operations. ECS and EKS are both in production here, and the consolidation question between them is live.
• Experience building SLO frameworks, error budgets, incident response, and DR programs from scratch, not inheriting them.
• Experience building or managing global, distributed engineering teams across time zones.
• Strong security fundamentals: cloud security architecture, IAM, Zero Trust, network segmentation, SAST/DAST, SIEM/EDR, and working knowledge of SOC 2, ISO 27001, NIST, or equivalent.
• A track record of cloud cost reduction through Fin Ops: unit economics, per-team budget accountability, and leadership-facing visibility.
• Startup or high-growth experience, and the ability to distinguish tech debt that matters from tech debt that can wait.
• Preferred: MLOps infrastructure (Kubeflow, Airflow, or equivalent), defense or government-adjacent compliance experience (NIST 800-171, CMMC, ITAR, FedRAMP), and relevant certifications (CISSP, CISM, CCSP).

Systems Thinking: Ability to visualize and design how AI agent systems connect, share data, and enable valuable customer outcomes at scale.

Grit: Self-starter, persistent problem solver, relentless in the pursuit of progress in ambiguous 0-to-1 product environments.

Customer Empathy: Deep understanding of how enterprise customers operate; asks the right questions and designs solutions that stick.

Innovator: Passionate about applying emerging AI capabilities to practical, measurable business problems before the patterns are written.

Adaptability: Comfortable operating in dynamic,…