AI Jobs Index
Data-driven insights into the AI job market, featuring top employers, trending AI skills, salary trends, and comprehensive market analysis
Jobs by AI Category
Distribution of AI jobs across major categories
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Frequently Asked Questions
Common questions about the responsible AI job market
What is responsible AI?
Responsible AI refers to the practice of designing, developing, and deploying AI systems in ways that are safe, ethical, transparent, and aligned with human values. It encompasses AI safety, AI ethics, AI governance, model risk management, and regulatory compliance.
What types of responsible AI jobs are available?
Responsible AI careers span four main categories: AI Risk & Controls (identifying and mitigating AI-related harms), AI Safety & Red Teaming (testing AI systems for failure modes), AI Governance & Programs (policy and compliance roles), and Model Risk Management & Validation (quantitative oversight of ML models used in regulated industries).
What skills are most in demand for AI jobs?
The most in-demand skills for responsible AI roles include machine learning fundamentals, Python, model risk frameworks (SR 11-7), AI policy and regulatory knowledge, red teaming and adversarial testing, risk assessment methodologies, and experience with large language models (LLMs).
What is the average salary for responsible AI jobs?
Salaries for responsible AI roles vary by specialization and seniority. Senior AI governance and model risk roles at large financial institutions typically range from $120,000–$200,000+. AI safety roles at leading AI labs can range from $150,000–$300,000+ including equity.
Which companies are hiring the most AI professionals?
Top hirers for responsible AI roles include major AI labs (Anthropic, OpenAI, Google DeepMind), large financial institutions (JPMorgan, Goldman Sachs, Bank of America), Big Tech companies (Microsoft, Meta, Amazon), and dedicated AI governance firms. The Responsible AI Jobs Index tracks the top employers by hiring volume and quality score.
How do I get a job in AI safety or AI governance?
Breaking into responsible AI typically involves building relevant technical or policy foundations: for AI safety, backgrounds in ML, computer science, or math are common; for AI governance, backgrounds in law, policy, or risk management are valued. Many professionals also complete AI safety fellowships, obtain certifications in AI risk management, or contribute to open-source AI safety research.
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