Amazon's AGI Information is seeking an exceptional Machine Learning Engineer to drive ML systems development in the Amazon Knowledge Graph (AKG) team. AKG is re-inventing knowledge graphs for the LLM-era, developing sophisticated ML models and pipelines that enable efficient LLM grounding and power LLM-based customer experiences.

As an ML Engineer on the AKG team, you'll design and implement highly scalable training and inference systems that process massive knowledge graph datasets. Working closely with applied scientists, you'll be responsible for transforming prototype models into production-ready systems, optimizing training pipelines, and building efficient serving infrastructure that enhances how Amazon's knowledge graph interacts with LLMs across the company.

We're looking for candidates who combine strong software engineering fundamentals with practical ML system development experience. You'll need to demonstrate expertise in building scalable, fault-tolerant distributed systems, with a track record of shipping production services that handle large-scale workloads. While ML engineering skills are important, we prioritize candidates who understand professional software engineering practices across the full development lifecycle - from system design and coding standards to testing, deployment, and operational excellence.


Key job responsibilities
- Design, develop and maintain ML model serving infrastructure to enable high-throughput, low-latency entity resolution predictions in production environments
- Collaborate with applied scientists to productionize ML models, including implementing model improvements and new architectures for entity matching and deduplication
- Develop efficient data processing pipelines to handle large-scale training and inference data for entity resolution models
- Support experimentation and A/B testing infrastructure to evaluate model improvements
- Work closely with downstream engineering teams to integrate entity resolution capabilities into various product surfaces
- Participate in code reviews, technical design discussions, and sprint planning to ensure high quality software delivery
- Strong understanding of ML fundamentals and common optimization techniques
- Experience with data processing and ETL pipelines at scale