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About the Role

Palona’s AI agents operate in real-world restaurant environments, facing challenges like noisy phone lines, varied accents, and complex menus. This role focuses on improving the intelligence, accuracy, safety, latency, and cost of these voice and multimodal agents through disciplined evaluation, data quality, modeling judgment, experimentation, and production feedback loops. Success is measured by production-ready improvements that enhance guest, restaurant, and business outcomes.

Responsibilities

Develop modeling and experimentation strategies for high-impact agent problems in voice, language, reasoning, ordering, multilingual behavior, and multimodal understanding.
Build rigorous offline and online evaluations that measure task completion, accuracy, safety, latency, cost, conversational quality, and business outcomes.
Create and maintain representative datasets from simulations, human annotation, production feedback, and difficult edge cases while protecting sensitive data.
Evaluate frontier and open-source models and make clear build, buy, route, prompt, fine-tune, or distill decisions.
Improve prompting, context construction, memory, tool-use policies, structured outputs, model routing, and fallback behavior.
Design fine-tuning, preference optimization, distillation, or other post-training work when it offers a measurable advantage over simpler methods.
Partner with speech and real-time engineers to improve ASR, TTS, turn-taking, interruption handling, pronunciation, multilingual behavior, and end-to-end latency.
Develop analysis tools that explain model failures, slice performance by scenario, detect regressions, and accelerate iteration.
Ship model changes with production guardrails, staged rollouts, monitoring, rollback paths, and clear quality gates.
Translate new research and model releases into concrete product opportunities and communicate tradeoffs to technical and non-technical partners.
Raise scientific and engineering standards through reproducible experiments, thoughtful reviews, and clear documentation.

Requirements

3+ years of industrial experience in a relevant technical domain.
Strong machine learning foundations and hands-on experience developing or evaluating production AI systems.
Strong Python skills and experience with modern ML tooling such as PyTorch, JAX, Hugging Face, or equivalent systems.
Practical experience with LLMs, speech models, multimodal models, or agentic systems.
Ability to design reliable experiments, define useful metrics, analyze noisy results, and avoid optimizing against weak proxies.
Experience building datasets, evaluation harnesses, model services, or training and inference pipelines.
Strong software engineering judgment; your work is reproducible, tested, observable, and usable by other engineers.
Ability to connect modeling choices to product constraints including latency, cost, privacy, safety, and user experience.
Comfort operating in ambiguity and collaborating across research, engineering, product, and customer contexts.
AI-native working habits and genuine curiosity about new model capabilities and limitations.

Skills

Python
PyTorch
JAX
Hugging Face
LLMs
Speech models
Multimodal models
Agentic systems
Machine learning
AI systems

Experience Level

3+ years of industrial experience

Salary/Compensations

Competitive Salary and Stock Option Plan

Benefits

Medical, dental, vision, retirement, leave, and disability benefits as applicable.
Family Leave
Short Term & Long Term Disability
Paid time off and company holidays.
Learning and development support.