AI Engineer- Responsible AI

Role Overview Build the Future of Safe and Responsible AI Are you an experienced AI engineer advancing the frontiers of AI safety, LLM jailbreak detection and defense, and agentic AI, with publications and production deployments to show for it? Join us to translate pioneering research into robust, scalable security systems and trustworthy LLM platforms that resist adversarial and behavioral exploits at enterprise scale. The Mission We're tackling cutting-edge AI safety across adversarial robustness, jailbreak defense, agentic workflows, and human-in-the-loop risk modeling. As an AI Engineer, you'll own high-impact projects from research conception through production deployment, directly contributing to our platform's security guarantees while building scalable, maintainable infrastructure. What You'll Do
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. Example Problems You Might Tackle
Production Red-Teaming Platform: Build and operate an automated red-teaming infrastructure that continuously probes advanced LLMs (GPT-4o, GPT-5, LLaMA, Mistral, Gemma) at scale, with dashboards and alerting.
Real-Time Defense Systems: Implement context-aware, multi-turn attack detection and guardrail mechanisms as low-latency services handling 10K+ requests per second.
Agent Self-Regulation at Scale: Develop agentic architectures for autonomous self-check and self-correct with distributed orchestration and fault tolerance.
Safety Data Platform: Design and build data infrastructure for collecting, storing, and analyzing petabyte-scale safety telemetry with streaming analytics. Minimum Qualifications
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 Preferred Qualifications
Experience in adversarial prompt engineering, jailbreak detection (narrative, obfuscated, sequential attacks)
Prior work on multi-agent architectures or robust defense strategies for LLMs in production environments
Experience with large-scale data processing frameworks (Spark, Flink, Kafka) and data warehousing
MLOps expertise: model serving (Triton, TensorRT, vLLM), experiment tracking (W&B, MLflow), and CI/CD for ML
Infrastructure as Code experience (Terraform, Pulumi) and DevOps best practices
Experience with distributed computing frameworks (Ray, Dask) for scalable training and evaluation
Familiarity with observability stacks (Prometheus, Grafana, DataDog) and incident management
First-author publications, strong GitHub profile, or significant open-source contributions Our Stack
Modeling: PyTorch/JAX, Hugging Face, vLLM, Mistral, LLaMA, OpenAI APIs
Safety: Red-teaming frameworks, LLM benchmarking (SODE, ART, HarmBench), human behavior simulation
Infrastructure: Kubernetes, Docker, Terraform, AWS/GCP, Ray, Spark
MLOps: Triton Inference Server, Weights & Biases, MLflow, Airflow, ArgoCD
Data: PostgreSQL, Redis, Kafka, Snowflake/BigQuery, dbt
Observability: Prometheus, Grafana, DataDog, PagerDuty What Success Looks Like
Production systems that measurably improve safety KPIs: adversarial robustness, over-defensiveness rates, and incident response latency
Publishable research outcomes (with company approval) demonstrating novel contributions to AI safety
Well-documented, tested, and maintainable code with comprehensive CI/CD and monitoring
Infrastructure that scales reliably and enables the broader team to iterate quickly on safety research Why Centific
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 Location: Palo Alto, CA or Seattle, WA (Remote) Employment Type: Full-Time Benefits:
Comprehensive healthcare, dental, and vision coverage
401k plan
Paid time off (PTO)
And more! Company Overview: Centific is a frontier AI data foundry that curates diverse, high-quality data, using our purpose-built technology platforms to empower the Magnificent Seven and our enterprise clients with safe, scalable AI deployment. Our team includes more than 150 PhDs and data scientists, along with more than 4,000 AI practitioners and engineers. We harness the power of an integrated solution ecosystem—comprising industry-leading partnerships and 1.8 million vertical domain experts in more than 230 markets—to create contextual, multilingual, pre-trained datasets; fine-tuned, industry-specific LLMs; and RAG pipelines supported by vector databases. Our zero-distance innovation™ solutions for GenAI can reduce GenAI costs by up to 80% and bring solutions to market 50% faster. Our mission is to bridge the gap between AI creators and industry leaders by bringing best practices in GenAI to unicorn innovators and enterprise customers. We aim to help these organizations unlock significant business value by deploying GenAI at scale, helping to ensure they stay at the forefront of technological advancement and maintain a competitive edge in their respective markets. Learn more about us at centific.com. Centific is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, ancestry, citizenship status, age, mental or physical disability, medical condition, sex (including pregnancy), gender identity or expression, sexual orientation, marital status, familial status, veteran status, or any other characteristic protected by applicable law. We consider qualified applicants regardless of criminal histories, consistent with legal requirements.