Why Join Us?

• Growth: With a strong emphasis on personal development, we encourage continuous learning and tackling challenging projects that contribute to your professional growth.
• Impact: Play a pivotal role in shaping the future of our technology and products, directly influencing the success of our solutions.
• Culture of experimentation: We live by the mantra “fail fast, fail often”. By encouraging experimentation and learning from each failure, we pave the way for significant breakthroughs. We promote an environment where every team member is empowered to test new ideas, challenge the status quo and contribute to a culture of continuous innovation.

 

What you will do:

As a AI Engineer on the Computer Vision team, your primary focus will be contributing to the development and evaluation of cutting-edge computer vision models and infrastructure that power our AI-driven inspection platform. You’ll work alongside experienced engineers to tackle real-world problems in object detection, semantic segmentation, and image understanding. Here's a closer look at your responsibilities:

• Experiment with and prototype computer vision models: Design, train, and evaluate deep learning models for object detection, segmentation and classification on real-world infrastructure imagery.
• Support MLOps and data pipelines: Collaborate with the team to improve data preprocessing pipelines, model evaluation tools, and ML lifecycle tracking systems using tools like MLflow.
• Perform error analysis and quality improvements: Analyze failure modes in models and datasets, and contribute to strategies for improving performance across edge cases.

 

Growth and Exploration Opportunities:

• Advanced CV architectures: Explore and test state-of-the-art vision architectures including transformer-based vision models (e.g. ViTs, DINOv2, SAM3).
• Production AI exposure: Gain hands-on experience in taking AI models from experimentation to deployment, including learning about dataset versioning, reproducibility, and model performance monitoring in production-like environments.

 

Requirements:

• Experience building and deploying machine learning or computer vision solutions in real-world environments (production systems, customer-facing products, or internal platforms). Demonstrated experience through internships, research, or substantial projects is acceptable.
• Strong foundation in computer vision and deep learning, with hands-on experience in areas such as object detection, semantic segmentation, or image classification.
• Proficiency in Python and practical experience with deep learning frameworks such as Pytorch or Tensorflow
• Experience working with end-to-end ML workflows, including data preprocessing, model training, evaluation, and iteration.
• Comfortable working in ambiguous problem spaces and translating real-world constraints into technical solutions.
• Strong communication skills and the ability to collaborate effectively with engineers, product, and operational teams

 

Nice to Have:

• Experience deploying or supporting ML models in production (e.g., APIs, batch inference pipelines, edge deployment, or cloud-based systems).
• Hands-on experience with MLOps tooling such as MLflow, Weights & Biases, DVC, or similar experiment tracking and lifecycle tools.
• Familiarity with cloud platforms (GCP, AWS, or Azure) and containerized workflows (Docker).
• Experience with large-scale or high-resolution imagery, including aerial, satellite, or infrastructure inspection data.
• Experience improving model performance through data-centric approaches (dataset curation, labeling strategies, augmentation).

 

Details:

• Location: Miami (In Office)
• Note: This role is open to both junior and senior candidates. Scope, ownership and compensation will be calibrated based on experience.