Sr. Product Manager - Runtime Infra, AI/ML, Annapurna Labs

Amazon•Cupertino, CA

About The Position

AWS Trainium is deployed at scale, with millions of chips in production, and has been used for training and inference of frontier models. AWS Neuron is the software stack for Trainium, enabling customers to run deep learning and generative AI workloads with optimal performance and cost efficiency. AWS Neuron is hiring a Technical Product Manager to work backward from Trainium customers and drive the developer experience for running high-performance ML workloads at scale on AWS Trainium, from getting started with Neuron Deep Learning Containers, AMIs, and AWS services to operating at scale through orchestration, resiliency, and observability. You will drive the product strategy for how developers interact with Trainium through container ecosystems, resource management platforms, and AWS services. This includes Neuron integration with orchestration tools (SLURM, Kubernetes), AWS services (EKS, SageMaker), Neuron Deep Learning Containers and AMIs, and Linux distribution support. You will also drive the strategy for resiliency and observability tools that enable system diagnostics, performance monitoring, health monitoring, automated recovery, and telemetry, allowing customers to operate AI training and inference workloads with maximum uptime and efficiency, as well as how Neuron Runtime System interacts with ML frameworks to ensure scale and high performance execution of models. To be successful in this role, you will partner with engineering teams and PMs responsible for training, inference, and performance tools, Marketing, Business Development, and Solution Architects supporting customers, and develop deep knowledge and understanding of Trainium Architecture and Neuron Runtime System (including Neuron Runtime Library, Neuron Kernel Driver and Collective Communication Stack) to effectively define product strategy and make informed technical decisions. About AWS Neuron AWS Neuron is the software stack for running deep learning and generative AI workloads on AWS Trainium and AWS Inferentia. It includes a compiler, runtime, training and inference libraries, and developer tools for monitoring, profiling, and debugging. Built on an open source foundation, Neuron supports native PyTorch and JAX frameworks and popular ML libraries without code modification. Neuron enables rapid experimentation, distributed training across multiple chips and nodes, and cost-optimized inference powered by optimized kernels. For performance optimization, Neuron provides the Neuron Kernel Interface (NKI) for direct hardware access and a suite of profiling and debugging tools.

Requirements

Bachelor's degree in computer science, engineering, analytics, mathematics, statistics, IT or equivalent
10+ years of industry experience with at least 5+ years in Technical product management and 3+ years of software development
Solid knowledge in container orchestration and Kubernetes
Solid knowledge in computer architecture fundamentals and operating systems concepts
Excellent written and verbal communication abilities
Experience with Linux systems and kernel development
Track record of driving developer libraries
Experience with Machine Learning accelerators
Experience with concepts such as performance optimization, profiling and tooling
Experience with Deep Learning model training or inference.
Experience with distributed computing and parallel processing
Hands on experience with major ML framework: JAX or PyTorch
Familiarity with AWS services and cloud infrastructure engineering
Track record of driving open standards and ecosystem integration

Responsibilities

Product Strategy & Vision: Own product strategy and roadmap. Guide trade-offs between performance, scalability, and developer experience. Write PRFAQs and PRDs.
Customer Discovery: Understand deployment challenges, orchestration needs, and infrastructure pain points. Represent customer needs in executive prioritization.
Technical Leadership: Drive alignment across Neuron components (Runtime, Kernel Driver, Collective Communication, container infrastructure) and AWS services. Partner with training, inference, and performance PMs. Write user stories and define success metrics.
Impact: Enable customers (Anthropic, Databricks, AWS teams) to deploy, monitor, and operate ML workloads at scale through container orchestration, resource management, health monitoring, and observability.

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