Senior Applied Scientist - Agentic AI

Responsibilities:
• Perform end-to-end agentic system development: define agent goals and decomposition strategies; design planners, controllers, and executors; implement tool-use orchestration (APIs, SQL, vector search, code execution) and robust recovery/rollback.
• Advance planning and reasoning: hierarchical/task planning, self-reflection and critique, debate/tree-search methods, constraint satisfaction, and chain-of-thought/toolformer-style approaches to improve correctness, faithfulness, and robustness.
• Ground LLMs on unstructured data: build retrieval and indexing over documents, semi-structured data
• Ensure safety, privacy, and compliance: content safety policies, least-privilege tool access, execution sandboxes, prompt/memory redaction, PII handling, and governance appropriate for regulated enterprise settings; implement interpretable action logs.
• Productionize agentic solutions: collaborate with platform teams to ship planning/orchestration services and evaluation harnesses; implement observability, telemetry, canarying, rollback, and lifecycle management for agent workflows.
• Stay current with research and translate advances into production differentiators; mentor teammates and contribute to a culture of scientific rigor and impact.

Minimum qualifications
• PhD in Computer Science, Machine Learning, Statistics, Electrical Engineering, or related field with a focus relevant to LLMs, planning/reasoning, NLP, or autonomous/interactive systems.
• Experience (industry or applied research) building and deploying ML/LLM systems, including agentic workflows, retrieval/grounding, and evaluation at scale.
• Demonstrated expertise in agentic methods: multi-step planning, tool-use orchestration, reflection/critique, and structured reasoning (e.g., CoT, programmatic planning).
• Strong background in retrieval over unstructured data, RAG architectures, document preprocessing, indexing, and provenance tracking for accuracy, safety, and robustness.
• Proficient in Python and modern ML stacks: PyTorch/JAX, Transformers, vector databases/IR libraries, orchestration frameworks; solid software engineering practices and experimentation discipline.
• Track record of publications in top venues (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL) or equivalent demonstrated impact in production systems.

Preferred qualifications
• Experience designing data/feedback pipelines for agent evaluation: step-level labeling, trace audits, and active learning for hard cases; familiarity with bias/variance trade-offs.
• Knowledge of search/planning and decision-making: tree search, bandits for tool/model selection, off-policy evaluation for policy changes, and statistical testing for online experiments.
• Familiarity with LLM efficiency and serving: PEFT/LoRA/QLoRA, quantization, KV cache management, batching, speculative decoding, routing across models/skills, and throughput/latency trade-offs.
• Experience integrating safety/guardrails and policy enforcement: sandboxed tool execution, OAuth/secret management, rate-limiting, jailbreak/prompt-injection defenses, and privacy-preserving telemetry aligned with enterprise compliance.
• Comfortable collaborating across research, engineering, product, and legal/compliance; excellent communication skills to explain methods and results to technical and non-technical stakeholders.
• Practical experience with experiment tracking, model registries, CI/CD for ML, and production observability for agent traces and actions.

Career Level - IC3