AI Engineer – Responsible AI

🕒 Março 26

🏄 California, Washington – Remoto

🏄 California – Remote ☕ Washington – Remote

💵 $150.000 - $160.000 / ano

⏰ Tempo Integral

🟢 Júnior

🟡 Pleno

🤖 Engenheiro de IA

🦅 Patrocina Visto H1B

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Thermo Fisher Scientific

SiteLinkedInTodas as Vagas

10.000+ funcionários

⚕️ Seguro de Saúde

🧬 Biotecnologia

💊 Farmacêutico

Healthcare Insurance • Biotechnology • Pharmaceuticals

A Thermo Fisher Scientific é um fornecedor global líder de instrumentação científica, reagentes e consumíveis, além de serviços de software. Eles apoiam os setores de ciências da vida, saúde e química analítica, fornecendo soluções robustas para pesquisa laboratorial e processos de produção. Seus produtos e serviços inovadores abrangem uma gama de aplicações, incluindo diagnósticos, automação de fluxo de trabalho em laboratório e descoberta de medicamentos.

Descrição

• Advance AI Safety: Design, implement, and evaluate attack and defense strategies for LLM jailbreaks (prompt injection, obfuscation, narrative red teaming) and deploy them as production-grade services. • Build Scalable Safety Infrastructure: Architect and deploy distributed safety evaluation pipelines handling millions of requests, with real-time monitoring, alerting, and incident response capabilities. • Large-Scale Data Engineering: Design ETL pipelines for processing terabytes of safety-related data (attack patterns, behavioral logs, model outputs); build data lakes and feature stores for safety ML systems. • Evaluate AI Behavior: Analyze and simulate human-AI interaction patterns at scale to uncover behavioral vulnerabilities, social engineering risks, and over-defensive vs. permissive response tradeoffs. • Agentic AI Security: Build production workflows for multi-agent safety (agent self-checks, regulatory compliance, defense chains) spanning perception, reasoning, and action. • MLOps & Model Deployment: Deploy safety models to production using containerized microservices, implement CI/CD pipelines for model updates, and manage model versioning and A/B testing infrastructure. • Benchmark & Harden LLMs: Create reproducible, automated evaluation protocols for safety, over-defensiveness, and adversarial resilience across diverse models with continuous integration.

🎯 Requisitos

• Master's degree in CS/EE/ML/Security or related field (Ph.D. preferred) • 2+ years of industry experience in applied ML/AI research or ML engineering • Track record of publications in AI Safety, NLP robustness, or adversarial ML (ACL, NeurIPS, ICML, EMNLP, IEEE S&P, etc.) or equivalent applied research impact • Strong Python and PyTorch/JAX skills with experience deploying ML models to production • Demonstrated experience in at least one of: LLM jailbreak attacks/defense, agentic AI safety, adversarial ML, or human-AI interaction vulnerabilities • Experience with containerization (Docker, Kubernetes) and cloud platforms (AWS, GCP, or Azure) • Proven ability to take research from concept to code to production deployment with rigorous testing and monitoring.

🏖️ Benefícios

• Real Impact: Your research ships directly, securing our core features and AI infrastructure at scale • Research to Production: Bridge the gap between cutting-edge research and production systems • Mentorship: Collaborate with Principal Architects and senior researchers in AI safety and adversarial ML • Velocity + Rigor: Balance high-quality research with mission-critical product focus