Research Engineer/ Applied Scientist

Wisdom AI•San Mateo, CA

1d•Onsite

About The Position

WisdomAI is seeking exceptional Research Engineers who enjoy turning cutting-edge AI research into production systems used by enterprise customers. This role sits at the intersection of research and engineering, where you will design experiments, invent new approaches, and ship systems that directly impact customer experience. The company builds AI-powered analytics that put answers directly in the hands of the people closest to the business, explaining the 'why' behind insights to help teams move from data to decisions with speed, confidence, and context. WisdomAI is trusted by companies like Cisco, Patreon, and Rubrik.

Requirements

5+ years of experience in applied machine learning, NLP, large language models, or AI research, with a proven track record of shipping production AI systems.
Master's degree in Computer Science, Machine Learning, Artificial Intelligence, NLP, or a related field required; PhD is a strong plus.
Deep experience with modern LLMs and agent architectures.
Strong software engineering skills with production-quality Python and experience using modern AI frameworks.
Demonstrated ability to design rigorous evaluation frameworks, benchmark models, and make data-driven decisions balancing quality, latency, cost, and reliability.
Demonstrated ability to improve model cost/quality through experimentation—including retrieval optimization, fine-tuning, distillation, inference optimization, and agent design.

Nice To Haves

Publications at leading AI or NLP conferences (ACL, EMNLP, NeurIPS, ICML, ICLR, NAACL, CVPR) or meaningful contributions to open-source AI projects.
Experience adapting foundation models and deploying production-scale LLM systems.

Responsibilities

Design and run rigorous experiments, including ablations, benchmarking, and error analysis, to improve NL-to-SQL generation, retrieval, ranking, and agentic reasoning.
Advance multi-turn conversational understanding through improved context retention, entity resolution, conversational memory, and intelligent clarification strategies.
Build robust evaluation frameworks, including automated benchmarks, regression suites, and LLM-as-a-judge methodologies to measure and improve model quality.
Prototype, validate, and productionize ML techniques, including fine-tuning, distillation, retrieval optimization, and agent architectures.
Evaluate emerging foundation models and AI research, rapidly translating promising advances into production experiments.
Contribute to the broader AI community through technical reports, or conference talks.