Job Description
Shape the future of technology as our next AI Ethicist at FutureVision Labs. Join a pioneering team developing ethical frameworks for AI systems that will define the 2026 technological landscape. This role sits at the intersection of cutting-edge AI development and human-centered innovation, ensuring our products advance society responsibly.
We seek a visionary leader to embed ethics into our AI lifecycle, from algorithm design to deployment. You'll collaborate with world-class researchers, engineers, and policymakers to create governance models that balance innovation with accountability. Our hybrid work environment fosters deep collaboration while offering flexibility for focused work.
Compensation includes equity, comprehensive benefits, and a professional development budget of $10,000 annually. Help us build technology that serves humanity's best interests.
Responsibilities
- Develop and implement comprehensive AI ethics frameworks aligned with 2026 regulatory landscapes
- Conduct bias audits and impact assessments for machine learning models
- Design transparent explainability protocols for high-stakes AI systems
- Lead cross-functional ethics working groups with engineering and product teams
- Advise on emerging AI governance policies and industry standards
- Create educational materials promoting ethical AI practices across the organization
- Partner with external stakeholders on responsible AI initiatives
Qualifications
- PhD in Philosophy, Computer Science, Ethics, or related field with 5+ years applied AI ethics experience
- Deep expertise in fairness, accountability, and transparency (FAT) AI principles
- Proven track record implementing ethical AI governance frameworks in production environments
- Strong understanding of evolving AI regulations (EU AI Act, NIST frameworks)
- Exceptional communication skills translating complex ethical concepts to technical teams
- Published research in AI ethics or responsible technology development
- Experience with bias detection tools and model interpretability techniques