Scientist Jobs

About The Position

Requirements

• PhD or Master's degree 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 such as VGGT, MASt3R, and 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 including strong baselines, ablations, and sound evaluation on real-world data, including rare-event and heavily imbalanced problems.
• Strong proficiency in Python and PyTorch.
• Solid foundation in applied mathematics, including linear algebra, geometry, probability, statistics, and optimization.
• Clear written and verbal communication skills, including presenting research to technical and non-technical audiences and stakeholders.

Nice To Haves

• Publications at top-tier venues such as CVPR, ICCV, ECCV, NeurIPS, or 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, manufacturing, aerospace, food processing, medical imaging, or other fields with similar rare-defect or visual verification problems.
• Experience with world models, such as V-JEPA 2, NVIDIA Cosmos, or spatially grounded VLMs.
• Experience with VLAs and transformer-based action planners.

Responsibilities

• Own applied research on vision problems, including anomaly detection, zero- and few-shot recognition, and semantic segmentation, 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 strategies for low-label, long-tail settings, including annotation plans, active learning, and evaluation protocols that improve field performance.
• Systematically analyze and improve deployed vision systems; collaborate with engineering and product teams to bring 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.

Benefits

• PTO
• Health insurance
• Vision insurance
• Dental insurance
• Life and AD&D insurance
• 401(k) with matching