Amazon is hiring a Machine Learning Engineer to join its AGI Information division, working on the reinvention of knowledge graphs for the large language model (LLM) era. This U.S.-based role under Amazon.com Services LLC offers salaries ranging from $129,300 to $223,600 per year, depending on experience and location.
The position is part of the Amazon Knowledge Graph (AKG) initiative, which builds scalable AI-driven systems that underpin customer experiences at global scale. Successful applicants will help design distributed systems, develop ML pipelines, and deploy real-time inference solutions that ensure Amazon’s AI systems remain accurate, grounded, and customer-centric.
🌍 Background & Job Description
Amazon has been at the forefront of artificial intelligence, natural language processing, and distributed computing, deploying these technologies at some of the largest scales in the world. Within its AGI Information division, the Amazon Knowledge Graph team is rethinking how data and facts are structured to support LLM-based applications.
Unlike traditional knowledge graphs, which primarily served entity linking and structured relationships, Amazon’s new approach emphasizes real-time knowledge at scale. With billions of entities and relationships, this infrastructure supports critical applications such as product search, personalized recommendations, conversational AI, and grounding responses from generative models.
The Machine Learning Engineer will be at the heart of this transformation. This role combines hands-on coding, large-scale system design, and applied AI engineering, bridging the gap between research prototypes and production systems that serve millions of Amazon users daily.
Exciting Opportunity: Machine Learning Engineer – Knowledge Graphs at Amazon AGI Information (Salary US$129,300 – US$223,600)
📊 Key Tasks & Responsibilities
The role is diverse, demanding a mix of software engineering, ML deployment, and collaboration with applied scientists. Core responsibilities include:
1. Architecting AI/ML Systems at Scale
- Design large-scale distributed systems to transform raw, unstructured data into interconnected knowledge graphs.
- Ensure systems scale to billions of entities while maintaining efficiency, low latency, and fault tolerance.
2. Developing LLM-Assisted Knowledge Tools
- Build AI-assisted pipelines for fact extraction, ontology generation, and verification.
- Enhance LLM grounding, ensuring outputs are accurate and factual.
3. Building High-Performance ML Infrastructure
- Develop and optimize model serving infrastructure for high-throughput inference.
- Deploy production-grade ML systems with real-time performance guarantees.
4. Partnering with Applied Scientists
- Work alongside researchers to move new models into production.
- Implement new architectures for graph construction and knowledge mining.
5. Data Engineering & Pipelines
- Design robust ETL workflows for large-scale training and inference datasets.
- Handle complex, high-volume data pipelines to support ML experiments.
6. Experimentation & A/B Testing
- Support infrastructure for large-scale A/B testing of ML systems.
- Measure performance improvements through data-driven experimentation.
7. Engineering Best Practices
- Contribute to code reviews, agile sprint planning, and design discussions.
- Maintain high standards of code quality, operational excellence, and scalability.
This is not a research-only position—it emphasizes end-to-end ownership, from design and experimentation to deployment in systems that impact millions of users.
🎓 Qualifications
Education
- Required: Bachelor’s degree in Computer Science (or equivalent).
- Preferred: Master’s degree in Computer Science, Data Engineering, or related field.
Required Professional Experience
- 3+ years of professional (non-internship) software development.
- 2+ years of system design or architecture, including fault tolerance and scaling.
- Demonstrated track record of deploying ML systems in production.
Technical Skills & Expertise
- Expertise in distributed systems and scalable infrastructure.
- Strong foundation in ML fundamentals and optimization.
- Experience with ETL workflows, pipelines, and large-scale data processing.
- Familiarity with cloud-based ML deployment and real-time inference.
Preferred Experience
- Experience with graph databases such as Amazon Neptune, Neo4j, or equivalents.
- Knowledge of ML serving frameworks (e.g., Triton, TensorRT).
- Familiarity with LLM optimization and model compilation.
- Strong engineering practices in CI/CD, testing, and source control.
🌟 Additional Information
Amazon’s Mission & Culture
Amazon emphasizes a customer-obsessed, inclusive, and innovation-driven culture. Engineers are expected to take ownership, simplify complex systems, and deliver solutions that directly improve customer experiences.
Equal Opportunity Commitment
Amazon is an equal opportunity employer and makes hiring decisions without regard to race, gender, sexual orientation, disability, or legally protected status. Applicants with past criminal records are also considered under Fair Chance hiring laws.
Location & Application Process
- Employer: Amazon.com Services LLC
- Location: United States
- Application: Candidates must apply via the official Amazon Jobs portal, where accommodations for disabilities may also be requested.
🚀 Why This Role Stands Out
- Direct impact on LLM grounding and factual reliability.
- Opportunity to shape how knowledge graphs evolve in the generative AI era.
- Hands-on ownership of production-grade ML systems serving millions globally.
- Work at the intersection of AI research, engineering, and customer innovation.
📝 Conclusion
The Machine Learning Engineer (Knowledge Graphs, AGI Information) role at Amazon is a unique opportunity for professionals passionate about AI infrastructure, distributed systems, and real-time ML deployment. With salaries ranging from $129,300 to $223,600 per year, this position offers both technical challenge and global impact.
For engineers eager to redefine how machines structure knowledge, ensure LLM outputs remain reliable, and build AI systems at unprecedented scale, this role represents a career-defining opportunity.
🔑 Keywords
Amazon Machine Learning Engineer, Knowledge Graphs jobs, Amazon AGI Information careers, AI engineering jobs USA, LLM grounding, Amazon Knowledge Graph AKG, machine learning pipelines, ML serving infrastructure, Amazon AI jobs, distributed ML systems careers
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