Note: The job is a remote job and is open to candidates in USA. DigitalOcean is a cloud technology company that builds scalable cloud infrastructure and AI capabilities. The Principal Engineer, AI/ML Security will lead the company’s AI security strategy, serve as the internal expert for AI-related security risk, and guide security controls, threat modeling, adversarial testing, governance, and executive risk communication.
Responsibilities
- Partner with Product Security and Engineering leadership to define how AI security controls are integrated into DigitalOcean's customer-facing products
- Partner with Security Data Science and Security Engineering to build, deploy, and serve in-house AI/ML models, guardrails, tooling, and automations. Participate in ongoing monitoring of deployed models to detect drift, evaluate performance, and plan and implement improvements alongside model stakeholders
- Lead adversarial testing and AI red teaming, including jailbreaking, prompt-injection, evasion, and poisoning testing
- Lead security reviews of major AI product initiatives from design through launch, providing architectural sign-off and escalation guidance for high-risk decisions
- Threat modeling across the AI lifecycle: data ingestion, training/fine-tuning, evaluation, model serving, RAG, agent frameworks, and post-deployment monitoring
- Own and drive DigitalOcean's AI Security strategy and multi-year roadmap, defining where we invest, what risks we prioritize, and how AI security evolves as the threat landscape and product portfolio change
- Architect and lead the AI Risk & Governance framework: establishing policies, standards, and controls for the responsible development, deployment, and monitoring of AI/ML systems across DigitalOcean and Cloudways
- Represent AI security at executive and board-level briefings; communicate risk posture, program progress, and strategic decisions to the CISO and senior leadership in clear, business-contextualised terms
- Define DigitalOcean's position on emerging AI security topics including agentic AI, model supply chain risk, synthetic data integrity, and AI-enabled social engineering
Skills
- 10+ years of experience in cybersecurity, with at least 4-5 years focused on AI/ML security, adversarial machine learning, or security data science in a production cloud or technology environment
- Demonstrated track record of defining and driving AI or security programs at an organizational level - not just executing within them. You have owned a strategy, not just implemented one
- Deep, practitioner-level knowledge of adversarial ML attack and defence: evasion, poisoning, model extraction, membership inference, and prompt injection at both conceptual and implementation depth
- Ability to read, evaluate, and synthesise academic AI security research and translate it into concrete engineering guidance and organizational policy
- Experience engaging executive and senior leadership audiences on technical risk: you can translate complex AI threat scenarios into business-level impact and justify investment decisions accordingly
- Strong programming skills in Python and Go with a track record of building security tooling and automation, not just reviewing others' code. You can stand up adversarial testing, detection, and guardrail capabilities yourself
- Proficiency reviewing and critiquing AI/ML system architecture (data ingestion, feature engineering, training pipelines, model serving, continuous monitoring) made by ML engineers and data scientists
- Hands-on experience with AI red-team and defensive tooling and with model supply-chain frameworks
- Bachelor's or Master's degree in Computer Science, Information Security, Mathematics, or a related field - or equivalent depth of practical experience
- Fluency in OWASP LLM Top 10, MITRE ATLAS, NIST AI RMF, ISO/IEC 42001, and the EU AI Act
- Prior experience in a cloud provider, security product company, or large-scale internet platform where AI/ML systems operate at significant customer-facing scale
- Familiarity with LLM security in production: securing RAG pipelines, agentic frameworks, and model APIs against prompt injection, jailbreaking, and data exfiltration
Benefits
- Organizational development team resources to support continued growth and development
- Reimbursement for relevant conferences, training, and education
- Access to LinkedIn Learning's 10,000+ courses to support continued growth and development
- Employee Assistance Program
- Local Employee Meetups
- Flexible time off policy
- You may qualify for a bonus in addition to base salary; bonus amounts are determined based on company and individual performance
- Equity compensation for eligible employees, including equity grants upon hire
- Option for eligible employees to participate in the Employee Stock Purchase Program
- Remote work
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