Location: Remote or Palo Alto, CA

Duration: 12-16 weeks (flexible)

Compensation: Paid, competitive

Start: Rolling

About Palona

Palona 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 Scope

This 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)

Sequential Decision Making
Reinforcement learning, planning, or control
POMDPs or decision-making under partial observability
Credit assignment with delayed and sparse rewards
Long-horizon optimization

Relevant signals

Publications in RL, planning, or control venues
Experience implementing and evaluating decision-making agents
World Modeling and State Representation
Latent state models for dynamic environments
Temporal abstraction and hierarchical representations
Persistent memory or state tracking
Modeling environments that evolve over time
Research on state-space models, memory-augmented models, or temporal representations
Reasoning Under Uncertainty and Causality
Belief state estimation
Uncertainty modeling in dynamic systems with incomplete or noisy information
Research in probabilistic modeling, causal inference, or dynamic systems
Multimodal Learning in Real Environments
Vision-language models
Learning from asynchronous, noisy, or partially missing modalities
Sensor fusion or multimodal representation learning
Publications or projects involving multimodal models
Experience working with real-world (not synthetic-only) data

What You Will Work On

Projects are scoped based on your expertise and may include:

Designing world state representations that persist across time, entities, and events
Modeling cause and effect in real operational workflows
Building reasoning systems that operate with partial observability and delayed outcomes
Developing evaluation methods for agents running in production
Translating research ideas into systems that are deployed and iterated on

You will collaborate closely with senior researchers and engineers and see how your work affects system behavior in the real world.

What We Look For

Strong problem formulation skills
Ability to connect theory with implementation
Comfort working with ambiguity and evolving research questions
Thoughtful evaluation and reflection on system behavior over time

What You Will Gain

Exposure to research problems shaped by real deployment constraints
End-to-end ownership from research idea to production impact
Close mentorship from experienced AI practitioners
Opportunity for continued research collaboration beyond the internship

How To Apply

Please include

CV
Google Scholar or publication list
A short statement (1-2 paragraphs) describing:
Your primary research focus
Why you are interested in real-world, production-grounded AI research

Requirements

Required

Current PhD student in CS, AI, ML, Robotics, or a closely related field
Strong research record (publications or equivalent contributions)
Hands-on experience implementing research ideas in code
Solid foundations in machine learning and statistical reasoning

Preferred

Experience with deployed or real-world ML systems
Prior industry or applied research experience
Strong Python and ML systems skills

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Real-World AI Research Intern (PhD)