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 an AI researcher (AI Research / Engineering) to lead novel agent + data research and turn it into real, scalable systems. You'll work at the intersection of agentic systems, environment scaling, and self-improving data loops—designing new training/evaluation environments, discovering the most informative data to collect next, and building the infrastructure that makes the loop reliable and repeatable. This is a "research that ships" role: you'll prototype, validate, and then productionize. You'll collaborate directly with founders, experts from academia/industries, and own projects end-to-end—from idea → experiments → core platform.
What You'll Do
- Lead novel research on agentic systems and self-evolving data loops (offline evals + production signals → next data batch)
- Work with leading customers to design and curate high-quality data and standardize the data curation pipelines.
- Design and scale environments for agents (synthetic + real-world, tool-use, long-horizon tasks, multi-step reasoning, multimodal where useful)
- Build training + evaluation pipelines: metrics, rubrics, automated judging, failure clustering, regression tests, and dashboards
- Develop data generation and curation systems: schema design, quality filters, safety checks, dedup, provenance, and preference signals
- Explore environment scaling methods (curricula, task generation, distribution shaping, difficulty calibration, reward design)
- Prototype quickly (tight iteration loops), then harden into production-grade components
- Collaborate across product/engineering to ship features that customers actually use
What We're Looking For
- Experience building LLM agents or agentic workflows (planning, tool use, memory, reflection, task decomposition)
- Research or engineering background in RL / RLHF / RLAIF environments, evaluation, or training data systems
- Ability to design novel experiments and run them end-to-end (hypothesis → ablation → analysis → decision)
- Strong coding skills in Python (plus one of: C++ / Rust / Go); comfortable owning production systems
- Familiarity with modern ML stacks: PyTorch, distributed training, inference serving, experiment tracking, PySpark
- Experience with data pipelines and infra: SQL, Spark/Ray, queues, vector stores, cloud (AWS/GCP), CI/CD
- Clear communication and strong taste for metrics, rigor, and iteration speed
- Ability to communicate clearly and work well in a team
Why Join Us
- Work on one of the hottest problems in AI: Build and scale agentic AI systems that push the frontier of what autonomous, reasoning-driven software can do in the real world
- Own product and infrastructure end to end: Take ideas from zero → one → scale, shaping core product decisions, system architecture, and production infrastructure
- High-growth, high-impact team: Join a small, fast-moving team where your work ships quickly and materially impacts customers and the company's trajectory
- Direct collaboration with founders: Influence strategy and help define the long-term technical and product vision
- Startup upside and velocity: Experience rapid learning, ownership, and career acceleration
- Competitive compensation and benefits: Strong salary, 401(k), and comprehensive medical, vision, and dental coverage
- Flexible work culture: Hybrid work, unlimited PTO, and a focus on trust, autonomy, and outcomes
- Inclusive and collaborative environment: We value thoughtful debate, diverse perspectives, and building great things together—without ego
How to Apply
We encourage candidates from all backgrounds to apply.
We are an equal opportunity employer and value diversity at our company.