Applied ML Engineer

Responsibilities

• Design and implement the transformer-based architecture for the semantic segmentation of aerial imagery.
• Develop efficient image-to-token and token-to-image conversion pipelines.
• Create and maintain training datasets, including data cleaning, augmentation, and validation.
• Optimize model training processes for distributed computing environments.
• Implement efficient inference pipelines for production deployment.
• Collaborate with the engineering team to integrate new models into existing infrastructure.
  

Requirements

• 5+ years of hands-on deep learning experience.
• Track record of successfully deploying computer vision models in production.
• Experience with vision transformer architectures.
• Experience optimizing models for production using ONNX/TensorRT.
• Background in handling high-resolution satellite/aerial imagery preferred.
• Master's/PhD in Computer Science, Machine Learning, or related field preferred.
  

Required Technical Skills

• Strong foundation in computer vision and deep learning fundamentals.
• Extensive experience training transformer models from scratch.
• Expert-level proficiency in PyTorch.
• Experience with ONNX or TensorRT model optimization and deployment.
• Deep understanding of distributed computing and parallel processing.
• Advanced Python knowledge, including multi-threading and multi-processing optimization.
• Experience with semantic segmentation tasks.
• Proven track record of handling large-scale data processing.
  

Desired Qualities

• Strong mathematical foundation in deep learning concepts.
• Experience with model architecture design and optimization.
• Proven ability to conduct independent research and stay current with the latest developments.
• Excellence in technical documentation and communication.
• Self-motivated with a passion for solving complex technical challenges.
  

What Sets You Apart

• Experience with vision transformers specifically for segmentation tasks.
• Published research or contributions to open-source computer vision projects.
• Experience with high-performance computing environments.
• Background in geospatial data processing.
• Hands-on experience with model quantization and optimization using ONNX/TensorRT.
• Experience deploying optimized models in production environments.