Top AI Ethics Jobs in Los Angeles, CA

Lead founding AI Safety & Security Engineering team, define harness architecture and engineering standards, drive Find/Validate/Patch roadmap, mentor engineers, and translate research into production-grade, secure ML systems.

Lead end-to-end red-teaming campaigns against frontier AI models by defining threat models, designing attack scenarios and taxonomies, coordinating operators, and producing executive risk reports. Establish recurring testing cadences, track exploit families, measure severity and user harm, maintain dashboards, and partner with research, product, and operations teams to validate fixes and run regression tests.

Evaluate and improve the safety, security, and robustness of large language models. Responsibilities include designing jailbreak, prompt injection, adversarial, and tool-use abuse tests; developing regression suites and evaluation frameworks; identifying vulnerabilities; translating findings into safety recommendations; and documenting methodologies and results for technical and non-technical stakeholders.

Stress-test large language models by crafting adversarial prompts and multi-turn scenarios that expose safety, bias, hallucination, prompt injection, and guardrail vulnerabilities. Evaluate model responses using harm taxonomies and severity rubrics, document findings, refine testing methods, and collaborate with engineers, data scientists, and researchers to improve AI safety and robustness. The role involves regular exposure to disturbing harmful content.

Cinder is building a paid expert network for specialists who test AI models for safety risks. Projects may involve red teaming, jailbreak testing, image, video, audio, agentic, and MCP-connected systems. They seek experts in areas such as child safety, CBRNE, extremism, cybersecurity, fraud, self-harm, psychology, copyright, and multilingual or culturally specific model evaluation.

Evaluate and improve AI-generated outputs for accuracy, clarity, quality, and business alignment. Develop and refine prompts, annotate data, conduct rubric-based assessments, fact-check content, and provide actionable feedback on model performance. Create and edit technical documentation, strategy reports, market analyses, business cases, executive summaries, and professional communications. The role requires strong analytical reasoning, writing, research, quality assurance, and attention to detail.

Evaluate AI coding-agent interactions for usefulness, correctness, engineering judgment, reasoning quality, and user trust. Provide rigorous written feedback, distinguish response quality levels, help define evaluation standards, and record short video explanations. The role requires senior-level software engineering experience, familiarity with AI-assisted development tools, strong TypeScript/JavaScript or Python skills, and excellent English communication.

Designs enterprise data and AI governance operating models, risk-tiering frameworks, lifecycle stage gates, and practical controls aligned with major AI governance standards and regulations. Uses Microsoft Purview and Fabric to implement governance, privacy, security, and insider-risk controls. Partners with sales, marketing, and practice leaders to shape offerings, lead client engagements, and develop the consulting team.

Leads the company’s AI governance program by developing frameworks, policies, controls, inventories, approval processes, and monitoring for AI and LLM tools. Partners with security, engineering, IT operations, risk, legal, and compliance teams to manage AI risks, third-party due diligence, data protection, regulatory compliance, audits, training, and responsible AI adoption. Tracks emerging regulations and standards while measuring program effectiveness and driving continuous improvement.

Lead design and implementation of AI and data governance frameworks, privacy guardrails, metadata standards, and compliance with GDPR/HIPAA/CCPA/NIST AI RMF. Monitor and audit models for bias, explainability, and transparency; partner with engineers, stewards, and leaders to embed ethical decision-making, set KPIs, run audits, and produce dashboards and reports to drive governance adoption.