Top AI Ethics Jobs in Chicago, IL

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.

Leads trust and safety for AI products serving teachers and students. Owns model guardrails, evaluations, red-teaming, monitoring, incident response, student-facing safety, district trust documentation, and regulatory alignment. Builds safety infrastructure into product development, manages incidents and postmortems, partners with Legal and Engineering, and leads a growing Trust & Safety team.

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.

Leads enterprise AI, data, automation, and emerging technology strategy. Owns data architecture, master data governance, data quality, privacy controls, AI governance, self-service analytics, automation standards, tooling, budgets, vendors, and a global team. Builds a data and AI center of excellence, establishes policies and training, delivers automation use cases, and partners with security, legal, compliance, architecture, and business leaders to scale responsible technology adoption.

Develops and maintains AI governance policies, frameworks, and controls aligned with regulatory and industry standards. Conducts AI model risk assessments covering bias, explainability, data quality, and security; evaluates organizational and vendor risks; supports audits; designs controls; and partners with Legal, Privacy, Security, and Technology teams. The role also promotes compliant AI practices and organizational awareness while applying advanced AI/ML knowledge.