AI Research Engineer, Computer Vision & VLMs

Palona; Los Altos; On-site; EUR 70,000 - 110,000; Full time

Benefits offered by this job

Stock options

Green card sponsorship

Medical benefits

Family leave

PTO & holidays

Learning & dev

Job summary

Palona is seeking an AI Research Engineer to advance visual intelligence for restaurant environments. You will research computer vision and vision-language models, build datasets, train and evaluate models, and collaborate with product and engineering to deploy solutions.

You will own end-to-end experiments, reproduce results, and communicate tradeoffs. The role welcomes applicants from autonomous driving, robotics, or embodied AI, with a track record in CV/VLM research.

Qualifications

• 3+ years of research or applied development in computer vision or multimodal learning.
• Proven track record in CV or vision-language modeling via publications, projects, or industry work.
• Strong foundations in deep learning, visual representation learning, and experimental design.

Responsibilities

• Develop CV and VLM approaches for scene understanding, detection, tracking, and event understanding in video.
• Adapt and evaluate models for visual grounding, temporal reasoning, and structured prediction.
• Design training strategies: supervised fine-tuning, representation learning, distillation, domain adaptation.

Skills

Python

PyTorch

Deep learning

Computer vision

Vision-language

Experiment design

Data curation

Education

PhD in computer vision

Masters in ML

Tools

OpenCV

Git

Job description

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.

• 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.
• A demonstrated research track record in computer vision or vision-language modeling, through publications, substantial research projects, open-source contributions, or research delivered in industry.
• Strong foundations in deep learning, visual representation learning, and experimental design, with depth in areas such as video understanding, detection and tracking, visual grounding, or multimodal reasoning.
• Hands-on experience training, fine-tuning, or adapting computer vision models, and developing or evaluating VLMs beyond basic API integration.
• Strong Python skills and experience with PyTorch or an equivalent deep learning framework, along with modern training and evaluation tooling.
• Experience building datasets, designing reliable evaluations, analyzing model failures, and using ablations to understand what drives improvements.
• Strong software engineering judgment and the ability to turn research code into reproducible, tested systems that other engineers can use.
• Ability to connect modeling choices to product constraints including latency, cost, privacy, reliability, and user experience.
• Comfort working through ambiguity and collaborating across research, engineering, and product.

Especially relevant experience

• A PhD or research-focused master’s degree in computer vision, machine learning, robotics, or a related field, or equivalent research experience.
• Industry research or engineering experience in autonomous driving, robotics, embodied AI, or other applications of perception in the physical world.
• Publications at venues such as CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML, CoRL, ICRA, or RSS.
• Experience with monocular video perception, spatial understanding, long-video reasoning, or learning from limited and noisy labels.
• Experience shipping vision models under real-time constraints, including model compression, distillation, quantization, or inference optimization.

• Competitive salary and stock option plan.
• Company-sponsored green card applications for strong candidates hired into U.S.-based roles, subject to eligibility.
• Medical, dental, vision, and retirement benefits as applicable.
• Family leave and short-term and long-term disability benefits as applicable.
• Paid time off and company holidays.
• Learning and development support.