AI Engineer- Responsible AI

Role OverviewBuild the Future of Safe and Responsible AIAre 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 MissionWe'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 DoAdvance 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 TackleProduction 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 QualificationsMaster'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 engineeringTrack record of publications in AI Safety, NLP robustness, or adversarial ML (ACL, NeurIPS, ICML, EMNLP, IEEE S&P, etc.) or equivalent applied research impactStrong Python and PyTorch/JAX skills with experience deploying ML models to productionDemonstrated experience in at least one of: LLM jailbreak attacks/defense, agentic AI safety, adversarial ML, or human-AI interaction vulnerabilitiesExperience 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 monitoringPreferred QualificationsExperience in adversarial prompt engineering, jailbreak detection (narrative, obfuscated, sequential attacks)Prior work on multi-agent architectures or robust defense strategies for LLMs in production environmentsExperience with large-scale data processing frameworks (Spark, Flink, Kafka) and data warehousingMLOps expertise: model serving (Triton, TensorRT, vLLM), experiment tracking (W&B, MLflow), and CI/CD for MLInfrastructure as Code experience (Terraform, Pulumi) and DevOps best practicesExperience with distributed computing frameworks (Ray, Dask) for scalable training and evaluationFamiliarity with observability stacks (Prometheus, Grafana, DataDog) and incident managementFirst-author publications, strong GitHub profile, or significant open-source contributionsOur StackModeling: PyTorch/JAX, Hugging Face, vLLM, Mistral, LLaMA, OpenAI APIsSafety: Red-teaming frameworks, LLM benchmarking (SODE, ART, HarmBench), human behavior simulationInfrastructure: Kubernetes, Docker, Terraform, AWS/GCP, Ray, SparkMLOps: Triton Inference Server, Weights & Biases, MLflow, Airflow, ArgoCDData: PostgreSQL, Redis, Kafka, Snowflake/BigQuery, dbtObservability: Prometheus, Grafana, DataDog, PagerDutyWhat Success Looks LikeProduction systems that measurably improve safety KPIs: adversarial robustness, over-defensiveness rates, and incident response latencyPublishable research outcomes (with company approval) demonstrating novel contributions to AI safetyWell-documented, tested, and maintainable code with comprehensive CI/CD and monitoringInfrastructure that scales reliably and enables the broader team to iterate quickly on safety researchWhy CentificReal Impact: Your research ships directly, securing our core features and AI infrastructure at scaleResearch to Production: Bridge the gap between cutting-edge research and production systemsMentorship: Collaborate with Principal Architects and senior researchers in AI safety and adversarial MLVelocity + Rigor: Balance high-quality research with mission-critical product focusLocation: Palo Alto, CA or Seattle, WA (Remote)Employment Type: Full-TimeBenefits:Comprehensive healthcare, dental, and vision coverage401k planPaid 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 .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.