Principles Of Humanity Jobs in Washington DC

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Looking for Principles Of Humanity jobs in Washington DC? Browse our curated listings with transparent salary information to find the perfect Principles Of Humanity position in the Washington DC area.

Clinical Pharmacist

Company: CompHealth

Location: Washington State

Posted May 29, 2025

In order to apply, you will need to have either current or recent acute care hospital experience and an active Washington license.

Embedded Software Engineer II

Company: Honeywell

Location: Fort Washington, PA

Posted May 30, 2025

Your day-to-day work will involve full lifecycle software development, from concept and architecture through implementation, validation, and field support.

Frequently Asked Questions

What are typical salary ranges by seniority for Principles Of Humanity roles?
Junior (0‑2 years) $80‑110 k; Mid (2‑5 years) $110‑150 k; Senior (5‑10 years) $150‑200 k; Lead/Manager $200‑250 k; Director/VP $250‑350 k. Compensation varies by company size, location, and technical vs managerial focus.
What skills and certifications are required in this field?
Core skills: Python, R, TensorFlow/PyTorch, bias‑detection libraries (AI Fairness 360, Fairlearn), regulatory knowledge of GDPR, CCPA, ISO 27001. Certifications: Certified Ethical Emerging Technologist (CEET), Certified AI Ethics Professional (CAIEP), ISO 27001 Lead Auditor, GDPR Data Protection Officer.
Is remote work available for Principles Of Humanity positions?
Yes. Approximately 70 % of roles allow full‑remote or hybrid arrangements (3 days remote, 2 days on‑site for audit meetings). On‑site presence is mainly needed for compliance‑audit teams or client‑facing governance projects.
What are the career progression paths?
Typical ladder: Associate → Lead → Manager → Director → VP of Responsible AI. Lateral moves to product, compliance, or research tracks are common, with many professionals adding certifications or advanced degrees in ethics or data law.
What industry trends affect Principles Of Humanity careers?
Trends include tightening AI governance frameworks, mandatory explainability requirements, growing demand for fairness metrics in training pipelines, expansion of interdisciplinary ethics boards, and increased funding for AI ethics research in academia and industry.

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