Key ResponsibilitiesDesign and implement safety architectures for Agentic AI systems, including guardrails, reward modeling, and self‑monitoring capabilitiesLead and collaborate on alignment techniques such as inverse reinforcement learning, preference learning, interpretability tools, and human‑in‑the‑loop evaluationDevelop continuous monitoring strategies for agent behavior in both simulated and real‑world environmentsPartner with product, legal, Responsible AI, governance, and deployment teams to ensure responsible scaling and deploymentContribute to and publish novel research on alignment of LLM‑based agents, multi‑agent cooperation/conflict, or value learningProactively identify and mitigate failure modes, e.g., goal misgeneralization, deceptive behavior, unintended instrumental actionsSet safety milestones for autonomous capabilities as part of deployment readiness reviewsTechnical SkillsProficiency in SQL, Python, and data analysis/data mining tools. Experience with machine learning frameworks like PyTorch, JAX, ReAct, LangChain, LangGraph, or AutoGenExperience with high performance, large‑scale ML systemsExperience with deploying or auditing LLM‑based agents or multi‑agent AI systemsExperience with large‑scale ETLUse your skills to make an impactRequired QualificationsMaster's Degree and 4+ years of experience in research/ML engineering or an applied research scientist position preferably with a focus on developing production‑ready AI solutions2+ years of experience leading development of AI/ML systemsDeep expertise in AI alignment, multi‑agent systems, or reinforcement learningDemonstrated ability to lead research‑to‑production initiatives or technical governance frameworksStrong publication or contribution record in AI safety, interoperability, or algorithm ethicsPreferred QualificationsPh.