Applied Research EngineerThe next wave of competitive advantage isn't better general models. It's models that understand your business.At Eragon, we build company-specific AI systems trained on proprietary data, deployed in customer environments, and continuously improving through real-world use. Our models don't just respond.
They learn from interaction and get better over time.We've built a reinforcement learning framework called RLQF (Reinforcement Learning from Query Feedback) that turns real usage into training signal and creates a compounding improvement loop beyond static fine-tuning or RAG.The RoleAs an Applied Research Engineer, you'll design, train, and deploy models that power real business workflows.This is not research for its own sake. You'll work directly with customer data, constraints, and feedback to build systems that perform in production. You'll own the full lifecycle from problem framing and data design to training, evaluation, and iteration in the wild.What You'll DoTrain and adapt models: Fine-tune and post-train models on customer-specific data using RLQF and related techniquesClose the loop: Turn real user interactions, corrections, and workflows into training signalOwn end-to-end systems: Build from data ingestion and curation through training, evaluation, and deploymentEvaluate in production: Design evaluation frameworks that reflect real-world performance, not just benchmarksWork with customers: Partner directly with users to understand workflows and translate them into model behaviorShip and iterate: Continuously improve models based on live feedback and measurable outcomesWhat We're Looking ForStrong hands-on experience training, fine-tuning, or post-training ML modelsExperience working with messy, real-world data, not just clean benchmarksFamiliarity with reinforcement learning, feedback-driven training such as RLHF or RLAIF, or evaluation systemsAbility to move quickly from problem to data to model to iterationStrong engineering instincts and comfort owning systems end-to-endBias toward shipping and improving systems, not just running experimentsStrong Candidates Also HaveExperience fine-tuning or adapting large language models in productionBackground in agents, tool use, or workflow automation systemsExperience working in customer-facing or forward-deployed environmentsPrior startup or early-stage engineering experienceContributions to open source or side projects demonstrating applied ML depth