About AnalogyAI
We're building the data infrastructure behind self-evolving AI systems.
Our platform acts like an AI data researcher—automating the full lifecycle of data, from sourcing and curation to validation and delivery. Given a user prompt, it discovers and acquires relevant data sources, structures, and enriches them with task-specific schemas, runs rigorous quality and safety checks, and produces domain-specific, training-ready datasets and benchmarks.
Beyond data generation, we integrate directly with model training pipelines and production telemetry. By combining offline evaluations with real-world signals, our system automatically identifies failure modes and capability gaps, then generates the next most impactful dataset or incremental data batch to continuously improve the model.
We're building an inevitable data layer for enterprise AI—one that enables models to learn, adapt, and improve themselves over time.
About the Role
We're looking for brilliant AI Research Interns to join us at the intersection of agentic systems, environment scaling, and self-improving data loops.
As an intern at AnalogyAI, you won't be working in a vacuum. You will collaborate directly with our founders and world-class talents from both academia and industry to tackle "frontier" research problems. You will assist in designing new training environments, discovering informative data patterns, and building the experimental frameworks that power our self-evolving data loops. This is a unique opportunity to conduct "research that ships"—where your ideas are integrated into a core platform used by leading AI companies.
What You'll Do
- Collaborative Research: Partner with our researchers and engineers to explore novel agentic systems and self-evolving data loops.
- Frontier Experimentation: Design and execute rigorous experiments to validate hypotheses around data curation, synthetic data generation, and model evaluation.
- Environment Building: Assist in scaling environments for agents (tool-use, long-horizon tasks, multi-step reasoning, and multimodal workflows).
- Data Innovation: Help develop advanced quality filters, automated judging rubrics, and preference signal systems to identify high-signal data.
- Prototyping & Iteration: Build "proof-of-concept" features and tools that explore the limits of current LLM capabilities in a tight feedback loop.
- Knowledge Sharing: Present findings to the team and contribute to the technical direction of our data infrastructure.
What We're Looking For
- Academic Background: Currently pursuing a PhD or Master's in CS, AI, Math, or a related field (exceptional undergrads with strong research/coding backgrounds are welcome).
- Hands-on AI Experience: Familiarity with LLM agents and environments (planning, tool use, reflection) or experience in RL / RLHF / RLAIF.
- Technical Proficiency: Strong coding skills in Python; experience with PyTorch or similar ML frameworks.
- Analytical Rigor: Ability to think from first principles, design clean ablations, and communicate complex technical ideas clearly.
- Execution Mindset: You enjoy the "engineering" part of research—writing clean code and building tools that actually work.
- Curiosity: A deep interest in the "data side" of AI—understanding why certain data makes models better.
Why Join Us
- Mentorship: Work directly with founders and world-class researchers (including experts from top-tier labs and industry leaders).
- Impact: Your work won't sit in a slide deck; it will contribute to the data pipelines powering the next generation of AI models.
- Velocity: Experience the speed of a high-growth startup. We iterate daily and ship weekly.
- Community: Join a small, ego-free team that values thoughtful debate and diverse perspectives.
- Future Growth: High-performing interns will be fast-tracked for full-time return offers.
- Perks: Competitive intern stipend, office snacks, and team offsites in the heart of Menlo Park (CA).
How to Apply
We encourage candidates from all backgrounds to apply.
We are an equal opportunity employer and value diversity at our company.