Key job responsibilities

• Develop and optimize ML models for production deployment at massive scale
• Build and maintain ML pipelines and infrastructure for model training and inference
• Design and implement efficient data processing and feature engineering solutions
• Create and enhance ML evaluation frameworks and monitoring systems
• Collaborate on improving model performance, latency, and resource utilization
• Work with cross-functional teams to integrate ML solutions into product features
• Contribute to the development of ML platforms and tools used across Amazon
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A day in the life

As a Specialised Software Development Engineer, you'll immerse yourself in challenging projects that push the boundaries of technology.

Your day might involve learning from and collaborating with ML scientists to implement new model architectures, optimizing training pipelines for better efficiency, analyzing model performance metrics, and participating in design reviews for new ML features.

You'll work closely with product managers to understand requirements and with senior engineers to ensure your solutions meet Amazon's high standards for reliability and scalability.

Basic Qualifications

• Graduated within the last 24 months or expect to graduate in the next 6 months with a Bachelor's or Master's degree in Computer Science, Computer Engineering, or a related field,
• Strong knowledge of computer science fundamentals, including data structures, algorithms, and object-oriented design
• Proficiency in at least one programming language such as C, C++, Java, Python, or Rust
• Understanding of fundamental ML concepts and common frameworks (PyTorch, TensorFlow)
• Knowledge of data structures and software engineering principles
• Fluency in spoken and written English
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Preferred Qualifications

• Previous internship or project experience in ML/AI development
• Experience with NLP, Computer Vision, or Recommendation Systems
• Knowledge of ML deployment and MLOps practices
• Familiarity with distributed systems and cloud computing platforms
• Experience with deep learning model optimization
• Strong mathematical and statistical foundations
• Ability to effectively communicate technical concepts
• Experience with large-scale data processing
• Knowledge of ML evaluation metrics and A/B testing
• Passion for staying current with ML research and industry trends
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