AI Research Engineer, Computer Vision & VLMs

• 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.

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