Staff Applied AI Researcher - Agentic Reasoning Systems (India)
Dublin, CA (HQ)Full-timePosted 5mo ago
About the role
Staff Applied AI Researcher to define runtime reasoning and trustworthy autonomous decisioning for production, leading research across the core runtime intelligence capabilities behind ModelMesh™ including task decomposition, agent coordination, model routing, probabilistic decisioning, verification, observability-aware execution, and evaluation methods for enterprise reliability.
Responsibilities
- Set technical direction for agentic reasoning systems and runtime intelligence across ModelMesh™ — define orchestration strategies, decision policies, verification approaches, and runtime quality standards for massively parallel agent systems in production
- Architect the infrastructure for researcher augmentation at scale — design agentic platforms and orchestration primitives to deploy fleets of AI agents for experimentation, evaluation, and production integration
- Go deep: advance the science of autonomous reasoning — design, train, and refine learned components behind runtime decisioning (routing models, verification models, confidence estimators, reward models, policy selectors) with massively parallel agent-driven experiment pipelines
- Go broad: unify perception, retrieval, reasoning, and action — build repeatable methodology for composing domain-specific models, data perception systems, knowledge graphs, retrieval layers, and external tools into coherent agentic workflows
- Drive research on agent reliability for regulated environments — lead failure detection, self-checking, verification workflows, compounding error analysis, and auditable autonomous behavior research
- Define evaluation methodology for runtime intelligence — establish how task success, decision quality, robustness, traceability, and failure recovery are measured in enterprise conditions
- Influence platform-level architecture — shape decisions on model routing, tool use, observability, governance, access control, and interoperability with external agent ecosystems
- Mentor researchers across levels in the agentic paradigm — raise technical judgment, experimental rigor, and agent-augmented research practice; contribute to hiring
- Maintain hands-on research impact — sustain meaningful personal research contribution through publications, patents, and externally visible output
Qualifications
- Education: PhD or MSc in Computer Science, Machine Learning, AI, Robotics, or related field
- Experience: 8+ years in AI/ML research with impact on production systems, including 3+ years building LLM-based or autonomous AI systems
- Reasoning and orchestration: deep hands-on experience in at least two: multi-agent coordination, planning under uncertainty, sequential decision-making, probabilistic inference, model routing, or tool-using agent systems
- Evaluation of autonomous systems: designed evaluation frameworks for systems with non-binary correctness (decision quality, reliability under distribution shift, error rates, failure recovery in production)
- Systems at scale: designed and operated research systems integrating multiple models, data sources, and control mechanisms in production or near-production
- Software engineering: Python proficiency with strong architecture instincts; maintainable, testable, operable systems
- Technical leadership: set technical direction for a research area, mentored researchers, influenced quality standards beyond immediate team
Skills
Required:Multi-agent coordinationPlanning under uncertaintySequential decision-makingModel routingTool-using agent systemsPythonTechnical leadership
Nice to have:Probabilistic inference
Benefits
- Not specified in the description
Last updated Aug 5, 2026. Interested? Apply on the company site.
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