Applied Research AI Engineer

Job description

Glass Lewis is a trusted ally to more than 1,300 investors globally who use our corporate governance research, custom policy recommendations, engagement services and tools, and industry-leading proxy vote management solution to help drive value across their governance activities. We also work with over 3,000 corporate issuer clients, providing research reports, thought leadership, customized voting policies, equity plan models, and opportunities for direct engagement on material governance and disclosure practices.
 
Glass Lewis’ industry-leading research and analysis covers more than 30,000 meetings each year across approximately 100 global markets. Our clients include many of the world’s leading pension funds, mutual funds, and asset managers, collectively managing over $40 trillion in assets. We have teams located across the United States, Europe, and Asia-Pacific regions, giving us global reach with a local perspective on the most important governance issues. Founded in 2003, Glass Lewis is headquartered in San Francisco, California with additional offices in Kansas City, Missouri; London, U.K.; Paris, France; Limerick, Ireland; Karlsruhe, Germany; Stockholm, Sweden; Manila, Philippines; Toronto, Canada; Sydney, Australia; Timișoara, Romania; and Tokyo, Japan. Our team includes more than 400 full-time employees globally, over half of which are dedicated to research. For more information, please visit www.glasslewis.com.

We are hiring an Applied Research AI Engineer to complete our AI R&D team and make delivery sustainable at scale. 

You will own meaningful slices of our R&D 2026 roadmap - scaling the current system with new use cases, deepening core AI capabilities for automatic data acquisition and other in-house projects - while strengthening how we run research as a team.

Job requirements

• Strong applied NLP / LLM background plus comfort operating across retrieval, embeddings/vector stores, and graph-backed knowledge (depth in two of these is typical; curiosity across all three is important). Master / PhD is considered a significant plus.
• Demonstrated ability to mentor and raise the floor for a small team: code review habits, design docs, and pragmatic prioritization between “research interesting” and “ships safely.”
• Experience taking ML/LLM systems from experiment to production.

Job responsibilities

• Technical capacity across three research streams, in partnership with the team lead and ML engineer:
• Deep agents (reliable tool use, planning/evaluation, production-minded boundaries)
• Static and episodic knowledge graphs (modeling, construction/maintenance, querying + grounding)
• Retrieval-agent capability upgrades (vector retrieval, re-ranking, hybrid patterns, continuous improvement loops)
• Applied research with an engineering bar: hypotheses, controlled experiments, ablations, and benchmarks with clear metrics and reproducible setups.
• Operational deployment capability: move work from proof of concept toward MVP by validating on real production-like data, hardening interfaces, and integrating outcomes into the broader platform with pragmatic DevOps practices (CI, environments, deployment patterns, observability as needed).
• R&D operations that unblock everyone: experiment setup and tracking, dataset hygiene (cleaning, normalization, labeling workflows as applicable), summarization/reporting of findings, and acting as a bridge between research outputs and internal datasets the team generates and reuses.
• Standardization of R&D tooling: help define and maintain shared standards for notebooks/scripts/services, experiment tracking, dataset versioning practices, and reusable components so research accelerates without   becoming fragile or one-off. 

Job benefits

• Impactful Work on Advanced AI Systems
  Play a key role in developing next-generation AI solutions, including sophisticated agent architectures, knowledge graphs, and retrieval-augmented systems, delivering tangible impact at scale.
• Ownership and Independence
  Lead significant components of the 2026 R&D roadmap, with the autonomy to explore ideas, run experiments, and shape technical decisions.
• Collaborative and Research-Oriented Environment
  Partner with a highly capable, supportive team that prioritizes rigorous experimentation, open knowledge sharing, and high-quality engineering standards.