Sr. Computer Vision​/ML Scientist

Aicadium's Applied Research team is looking for a senior computer vision / ML scientist to tackle open-world visual understanding with limited labels across 2D recognition, multimodal inspection, and 3D/4D perception. You will bring anomaly detection, zero- and few-shot recognition, and semantic segmentation to applied systems used across multiple industries, building on 2D and 3D vision foundation models and vision-language models (VLMs).

You will also help shape the team's research roadmap, with a focus on 3D/4D reconstruction, spatial perception, and world models. This is a hybrid role based in San Diego, where our U.S. headquarters is located. The ideal candidate has deep hands-on experience in modern computer vision, has taken models beyond the prototype stage, and can own problems end-to-end while supporting the development of junior scientists.

• Own applied research on vision problems (anomaly detection, zero- and few-shot recognition, and semantic segmentation) end-to-end, from problem framing and baselines through validated models.
• Adapt and extend 2D and 3D vision foundation models and VLMs to industrial domains through fine-tuning, harness optimization, and distillation.
• Develop multimodal (vision-language) methods for industrial inspection tasks.
• Conduct applied research in 3D/4D reconstruction, spatial perception, world models, and physics-based simulations, from proof-of-concept prototypes to reusable internal capabilities.
• Design data and evaluation strategy for low-label, long-tail settings: annotation plans, active learning, and evaluation protocols that translate to stronger performance in the field.
• Systematically analyze and improve deployed vision systems. Collaborate with engineering and product teams to bring new models to production, and maintain and improve model quality after launch.
• Provide technical guidance to junior scientists and communicate methods, results, and trade-offs to technical and non-technical stakeholders.

• PhD or Master's in Computer Science, Electrical Engineering, Robotics, Machine Learning, or a related field with 4+ years of hands-on computer vision / ML experience; doctoral research counts toward this requirement.
• Expertise in at least two of the following: anomaly detection, zero- and few-shot (open-vocabulary) recognition, semantic segmentation, or multimodal (vision-language) learning.
• Experience adapting vision foundation models (e.g., DINOv3, JEPA-family models , SAM series) and VLMs (e.g., the Qwen-VL series) to new domains.
• Hands-on experience in 3D/4D reconstruction and spatial perception, including modern 3D foundation models (e.g., VGGT, MASt3R, Depth Anything
  3), grounded in multi-view geometry and camera models.
• Ability to independently define research problems and turn recent research into working prototypes, with rigorous experimental practice: strong baselines, ablations, and sound evaluation on real-world data, including rare-event and heavily imbalanced problems.
• Strong proficiency in Python and PyTorch, with a solid foundation in applied mathematics (linear algebra, geometry, probability, statistics, optimization).
• Clear written and verbal communication skills, including presenting research to technical and non-technical audiences and stakeholders.

• Publications at top-tier venues (CVPR, ICCV, ECCV, NeurIPS, ICLR) or notable open-source contributions in related areas.
• Experience bringing computer vision models from prototype to production or customer-facing deployment.
• Experience generating synthetic training data with generative models or simulation, particularly for defects, anomalies, and rare or data- deficient scenarios and classes.
• Domain experience in industrial inspection,…