Lexsi Labs is a frontier AI lab focused on building aligned, interpretable, and safe AI systems for real-world deployment. Our work spans agentic AI, alignment, interpretability, evaluation systems, enterprise AI, and foundational model research across structured, tabular, and enterprise data.
We build AI systems where transparency, auditability, robustness, and controllability are first-class constraints. Our environment is fast-moving, research-driven, and highly hands-on. We value people who bring open ideas, take ownership, and turn ambiguity into meaningful outcomes.
We are looking for AI Agent Interns to work on high-impact agentic AI problems across applied, research, and product directions.
This is not a narrow internship focused on building small internal bots. You will work on real agentic systems, evaluation infrastructure, enterprise use cases, research prototypes, and product tooling that can shape how AI agents are built, tested, aligned, and deployed.
Depending on your interests and strengths, you may contribute across one or more of three tracks:
On the applied side, you will work on high-impact enterprise agentic use cases. This may involve building agents that reason over complex workflows, interact with tools and data systems, execute multi-step processes, evaluate outcomes, and operate reliably in real-world environments.
Example work may include enterprise workflow agents, agents over structured or proprietary data, human-in-the-loop agent systems, decision-support agents, and applied prototypes that can evolve into production-grade components.
Success in this track means building useful, reliable, and high-value agentic systems that solve real enterprise problems.
On the R&D side, you will work on new ideas in agent architecture, harness design, evaluations, long-horizon reasoning, alignment, and interpretability.
This may include building new agent harnesses, designing evaluation frameworks, studying agent failure modes, benchmarking tool use and planning, or contributing to research papers, technical reports, and reusable research infrastructure.
Success in this track means producing research-quality insights, evaluation systems, benchmarks, or technical contributions that improve how agentic systems are understood and assessed.
On the product and tooling side, you will help build agents that improve how researchers, engineers, and users interact with Lexsi systems.
This may include an AlignTune Agent for alignment workflows, AI Scientist-style agents for hypothesis generation and experiment execution, tools for evaluating and debugging agents, or product prototypes that turn research capabilities into usable workflows.
Success in this track means building tools that compound the productivity of researchers, engineers, or enterprise users.
You will explore open-ended agentic AI problems and convert promising ideas into working prototypes, experiments, research artifacts, or product capabilities. You may build applied agents, design evaluations, create harnesses, experiment with planning and memory architectures, analyze failures, document findings, and collaborate with research, engineering, and product teams.
The expected outcome may be a production-grade applied component, a research contribution, a paper, a benchmark, an internal tool, or a product prototype.
We are looking for candidates with strong programming skills, preferably in Python, hands-on experience with LLMs or AI systems, and a strong interest in agents, evaluation, alignment, interpretability, or enterprise AI.
You should be comfortable working on ambiguous problems, building prototypes quickly, reading papers or technical material, documenting findings clearly, and independently identifying high-value directions.
Bonus signals include experience with agent frameworks, long-horizon agents, evaluation pipelines, research projects, open-source tools, backend systems, enterprise data, or AI safety and interpretability.
We move quickly and value substance over polish. This internship is for people who want to work near the frontier of agentic AI, bring original ideas, build seriously, test honestly, and create work that matters.
