The AGI Structured Information Retrieval team develops the services required to query the Amazon Knowledge Graph (AKG), an LLM-optimized, foundational component in the AGI tech stack, using Natural Language.
As a MLE, you'll have the power to lead the charge in developing algorithms and modeling techniques to push the boundaries of model training and deployment at scale.
Key job responsibilities
* Ability to quickly learn cutting-edge technologies and algorithms in the field of ML to participate in our journey to build the best NL to Structured retrieval service.
* Responsible for the development and maintenance of key platforms needed for developing, evaluating and deploying ML models for real-world applications.
* Work with other team members to investigate design approaches, prototype new technology and evaluate technical feasibility.
* Work closely with Applied scientists to develop and scale machine learning models.
A day in the life
You will be working with a strong team of engineers and collaborating with applied scientists to develop novel processes for constructing and enhancing structured information retrieval systems; and enable high precision/recall & low latency access to knowledge in AKG.
For this role we expect:
- 3+ years of professional software development experience in distributed systems with emphasis on ML infrastructure
- 3+ years of current programming experience building ML infrastructure using languages such as Python, C++ or Rust
- Hands-on experience with parallel computing platforms such as CUDA, OpenMP, etc
- Deep understanding of AI frameworks such as PyTorch, TensorFlow, and JAX, and their demands on underlying compute infrastructure, memory bandwidth, network interconnect, and storage as scale goes up
- Knowledge of emerging AI hardware accelerators and architectures
- Experience with containerization and orchestration technologies (Docker, Kubernetes)
As a MLE, you'll have the power to lead the charge in developing algorithms and modeling techniques to push the boundaries of model training and deployment at scale.
Key job responsibilities
* Ability to quickly learn cutting-edge technologies and algorithms in the field of ML to participate in our journey to build the best NL to Structured retrieval service.
* Responsible for the development and maintenance of key platforms needed for developing, evaluating and deploying ML models for real-world applications.
* Work with other team members to investigate design approaches, prototype new technology and evaluate technical feasibility.
* Work closely with Applied scientists to develop and scale machine learning models.
A day in the life
You will be working with a strong team of engineers and collaborating with applied scientists to develop novel processes for constructing and enhancing structured information retrieval systems; and enable high precision/recall & low latency access to knowledge in AKG.
For this role we expect:
- 3+ years of professional software development experience in distributed systems with emphasis on ML infrastructure
- 3+ years of current programming experience building ML infrastructure using languages such as Python, C++ or Rust
- Hands-on experience with parallel computing platforms such as CUDA, OpenMP, etc
- Deep understanding of AI frameworks such as PyTorch, TensorFlow, and JAX, and their demands on underlying compute infrastructure, memory bandwidth, network interconnect, and storage as scale goes up
- Knowledge of emerging AI hardware accelerators and architectures
- Experience with containerization and orchestration technologies (Docker, Kubernetes)