Real-World AI Research Intern (PhD)

Location: Remote or Palo Alto, CADuration: 12-16 weeks (flexible)Compensation: Paid, competitiveStart: RollingAbout PalonaPalona builds real-world AI systems that operate continuously in production. Our work focuses on AI agents that perceive, reason, remember, and act in physical environments, starting with restaurants as a constrained but high-signal domain.We are interested in research that survives contact with reality: partial observability, delayed effects, noisy signals, non-stationarity, and long-horizon outcomes.Research ScopeThis internship is for PhD students who want to work on applied research problems grounded in deployed systems.You will work on questions that arise from live AI agents operating in the real world, where clean assumptions break and system behavior must be understood over time, not just measured offline.Required Research Background (PhD Level)We are looking for candidates with deep research experience in at least one primary area, and working familiarity with adjacent areas.Primary Research Areas (at least one required)1. Sequential Decision MakingReinforcement learning, planning, or controlPOMDPs or decision-making under partial observabilityCredit assignment with delayed and sparse rewardsLong-horizon optimizationRelevant signals:Publications in RL, planning, or control venuesExperience implementing and evaluating decision-making agents2. World Modeling and State RepresentationLatent state models for dynamic environmentsTemporal abstraction and hierarchical representationsPersistent memory or state trackingModeling environments that evolve over timeResearch on state-space models, memory-augmented models, or temporal representations3. Reasoning Under Uncertainty and CausalityBelief state estimationUncertainty modeling in dynamic systems with incomplete or noisy informationResearch in probabilistic modeling, causal inference, or dynamic systems4. Multimodal Learning in Real EnvironmentsVision-language modelsLearning from asynchronous, noisy, or partially missing modalitiesSensor fusion or multimodal representation learningPublications or projects involving multimodal modelsExperience working with real-world (not synthetic-only) dataWhat You Will Work OnProjects are scoped based on your expertise and may include:Designing world state representations that persist across time, entities, and eventsModeling cause and effect in real operational workflowsBuilding reasoning systems that operate with partial observability and delayed outcomesDeveloping evaluation methods for agents running in productionTranslating research ideas into systems that are deployed and iterated onYou will collaborate closely with senior researchers and engineers and see how your work affects system behavior in the real world.What We Look ForStrong problem formulation skillsAbility to connect theory with implementationComfort working with ambiguity and evolving research questionsThoughtful evaluation and reflection on system behavior over timeWhat You Will GainExposure to research problems shaped by real deployment constraintsEnd-to-end ownership from research idea to production impactClose mentorship from experienced AI practitionersOpportunity for continued research collaboration beyond the internshipHow to ApplyPlease include:CVGoogle Scholar or publication listA short statement (1-2 paragraphs) describing:Your primary research focusWhy you are interested in real-world, production-grounded AI researchRequirementsRequiredCurrent PhD student in CS, AI, ML, Robotics, or a closely related fieldStrong research record (publications or equivalent contributions)Hands-on experience implementing research ideas in codeSolid foundations in machine learning and statistical reasoningPreferredExperience with deployed or real-world ML systemsPrior industry or applied research experienceStrong Python and ML systems skills