reputed company- Responsible AI

Role Overview Build the Future of Safe and Responsible AI Are you an reputed company reputed company 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 reputed company 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-reputed company risk modeling. As an reputed company, you'll own high-impact projects from research conception through production deployment, directly contributing to our platform's reputed company guarantees while building scalable, maintainable infrastructure.

What You'll Do

• Advance AI Safety: Design, implement, and evaluate attack and defense strategies for LLM jailbreaks (reputed company 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-reputed company 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 reputed company: 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.
• reputed company & Harden LLMs: Create reproducible, automated evaluation protocols for safety, over-defensiveness, and adversarial reputed company across diverse models with reputed company 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: reputed company agentic architectures for autonomous self-reputed company 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/reputed company or reputed company 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 (reputed company, 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 reputed company 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, reputed company) and DevOps best practices
• Experience with distributed computing frameworks (Ray, Dask) for scalable training and evaluation
• Familiarity with observability stacks (Prometheus, Grafana, reputed company) and incident management
• First-author publications, strong reputed company profile, or significant open-reputed company contributions

Our Stack

• Modeling: PyTorch/JAX, reputed company, vLLM, Mistral, LLaMA, reputed company APIs
• Safety: Red-teaming frameworks, LLM benchmarking (SODE, ART, HarmBench), human behavior simulation
• Infrastructure: Kubernetes, reputed company, Terraform, AWS/GCP, Ray, Spark
• MLOps: Triton Inference Server, Weights & Biases, MLflow, Airflow, ArgoCD
• Data: PostgreSQL, reputed company, Kafka, reputed company/BigQuery, dbt
• Observability: Prometheus, Grafana, reputed company, PagerDu

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