This is a Senior Staff individual contributor role. You will be hands on, setting technical direction across teams and writing production code focused on privacy engineering, applied ML, and sensitive data detection.
Responsibilities
Assess, benchmark and select ML models and techniques that meet our privacy requirements.
Build representative evaluation datasets and compare detection coverage, false positives, latency, cost and the impact on product quality.
Own the implementation of those models in our products, from prototype through production.
Train or fine-tune where needed, build the surrounding services and APIs, and optimize inference performance and reliability.
Build and deploy sensitive data detection, masking, redaction and tokenization for structured and unstructured data across logs, traces, APIs, analytics and annotation tools.
Manage day-to-day privacy operations intake, triage, routing, follow-up, and closure for matters assigned to the Privacy Office, ensuring clear ownership, timely status updates, and audit-ready documentation.
Operate and improve the lifecycle for DPIAs, PIAs, LIAs, TIAs, ROPAs, data mapping, vendor privacy reviews, and AI or automated decision-making assessments, with emphasis on consistent process, quality, evidence, and timely remediation.
Serve as a hands‑on privacy operations and tooling lead by configuring, administering, improving, or helping implement privacy management, GRC, workflow, data mapping, DSR, consent, preference, cookie, reporting, or evidence management tools.
Build efficient and repeatable workflows using available technology, including Microsoft 365 tools, Power Automate, Power BI, Excel, SharePoint, workflow management systems, ticketing tools, dashboards, templates, playbooks, and structured repositories.
Partner with IT, Cybersecurity, Enterprise Architecture, Digital, and business teams to translate privacy requirements into practical system, process, data flow, access, retention, logging, consent, and data minimization controls.
Requirements
Bachelor’s degree required; degree in computer science, software engineering, data protection/privacy, cybersecurity, information governance, regulatory compliance, business, or a related field preferred.
Group Product Manager, AI Security and Privacy Platforms, DeepMind
Lead a high-leverage product roadmap embedding agentic security and privacy-preserving architectures directly into the core AI lifecycle, including frontier models, autonomous agent frameworks, and confidential compute ecosystems.
Define and own the multi-quarter product goal, charter, and roadmap for Agentic Security and Privacy Platforms across frontier training and evaluation pipelines, developer tools, and deployments to end-users.
Build secure execution environments across evaluation, reinforcement learning, and developer command-line interface workflows by enforcing hardened sandboxes, strict network access controls, and least-privilege permissions.
Lead the compliance program for privacy and security controls for EU/UK users, children, and teens across Beast Industries products.
Compensation
Compensation: Base of $235K-$294K +20% bonus + equity
Responsibilities
Translate GDPR and COPPA requirements into specific, testable security controls.
Design and run recurring control tests, including sampling, evidence collection, and re-performance, across apps, websites, SDKs, analytics, advertising, and vendor integrations.
Verify age-gating, verifiable parental consent, data minimization, retention, deletion, and parental access flows for users under 13.
Own the security side of GDPR Article 32 and Article 25.
Maintain and test controls for data subject requests, records of processing, retention, and cross-border transfers.
Lead unified runtime monitoring platforms—combining reasoning-trace oversight, prompt injection defenses, and fine-grained context lineage tracking.
Scale automated adversarial testing platforms to systematically stress-test agent behaviors, paired with verification and safe production deployment.
Lead the platform roadmap for hardware-enforced confidential artificial intelligence inference—including secure hardware enclaves, remote attestation, and encrypted transport—alongside privacy and safety guardrails for enterprise and sovereign clouds.
Requirements
Bachelor's degree in Computer Science
Engineering
a related technical discipline, or equivalent practical experience
Nice to have: real-time safety guardrails
Nice to have: verifiable security and privacy controls
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