AI Research Engineer, Computer Vision & VLMs

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.