AI Research Engineer

Palona is building AI for the physical world, starting with restaurants. Understanding a busy restaurant means making sense of people, objects, activities, and events as they change over time, despite occlusion, changing lighting, varied camera views, and incomplete information.

We are looking for an AI Research Engineer with a strong research background in computer vision and vision-language models (VLMs) to develop the visual intelligence behind Palona’s products. You will work on image and video understanding, spatiotemporal reasoning, and multimodal models that connect visual observations to useful insights and actions in real restaurant environments.

This role combines research depth with ownership of working systems. You will formulate research questions, build datasets, train and evaluate models, and partner with product and engineering to bring successful approaches into production. Researchers and engineers from autonomous driving, robotics, embodied AI, and related perception fields are especially encouraged to apply.

What you’ll own

• Develop computer vision and VLM approaches for scene understanding, object detection and tracking, activity recognition, and understanding events across video.
• Adapt, fine-tune, and evaluate vision and vision-language models for visual grounding, temporal reasoning, and structured prediction grounded in observable evidence.
• Design training and adaptation strategies, including supervised fine-tuning, representation learning, distillation, and domain adaptation, based on measurable product needs.
• Build representative image and video datasets, annotation workflows, and evaluation sets that capture difficult edge cases while protecting sensitive data.
• Create rigorous experiments and benchmarks that measure perception quality, temporal consistency, hallucinations, robustness, latency, and cost across locations and operating conditions.
• Diagnose failures caused by occlusion, lighting changes, camera placement, rare events, and domain shift; use those findings to improve data and models.
• Partner with infrastructure and product engineers to deploy efficient inference pipelines, with monitoring, quality gates, staged rollouts, and rollback paths.
• Translate advances in computer vision, VLMs, and embodied AI into practical product capabilities, and communicate the evidence and tradeoffs behind your decisions.
• Raise research and engineering standards through reproducible experiments, thoughtful reviews, and clear documentation.

Requirements

• 3+ years of research or applied development experience in computer vision, multimodal learning, or a closely related field; relevant graduate research counts toward this experience.