Autonomy Engineer - Deep Learning Infrastructure

Responsibilities

• Develop high-performance deep learning inference solutions for computer vision workloads, delivering high throughput and low latency across hardware platforms.
• Profile computer vision and vision-language models to identify bottlenecks, acceleration and optimization opportunities, and improve inference power efficiency.
• Design and implement end-to-end MLOps workflows for model deployment, monitoring, and retraining.
• Apply machine learning expertise to training and runtime frameworks and model-efficiency tools to improve system performance.
• Develop methods for improving training efficiency.
• Implement GPU kernels for custom architectures and optimized inference.
• Design and implement SDKs that enable customers and external developers to create autonomous workflows using machine learning.
• Uphold and improve engineering standards.

Requirements

• Hands-on experience with MLOps, machine-learning inference acceleration and optimization, and edge deployment.
• Strong knowledge of deep-learning fundamentals, techniques, and current deep-learning architectures.
• Strong fundamentals in computer vision, image processing, and video processing.
• Hands-on experience building and managing machine-learning pipelines for vision or vision-language tasks, including data preparation, model training, model deployment, and monitoring.
• Understanding of security and compliance requirements in machine-learning infrastructure.
• Experience with machine-learning frameworks and libraries.
• Ability to drive concepts through the software lifecycle, including architecture, development, testing, deployment, and monitoring.
• Ability to navigate and deliver within complex codebases.
• Strong communication and collaboration skills.