Principal Product Manager - AI​/ML Training, Annapurna Labs; AWS

AWS Trainium is deployed at scale, with millions of chips in production, 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 Principal Technical Product Manager to define and drive product strategy for training software on Trainium, including distributed training libraries, post‑training workflows, reinforcement learning frameworks, and training performance optimization.

Your mission is to enable researchers and operators to train frontier models at scale on Trainium, from single‑node experimentation to distributed training across thousands of nodes.

You will be the champion inside AWS for frontier model builders pushing the bounds of scale and resilience for current and emerging training paradigms. You will work with customers inside and outside the company to identify key improvements and stay ahead of the training landscape. You will define how Neuron supports the training AI/ML ecosystem and what tools customers will use for their training workflows on Trainium.

To be successful, you will partner with engineering teams building training libraries and distributed training infrastructure, applied scientists developing optimization techniques, and PMs responsible for compiler, runtime, NKI, and infrastructure. You will develop deep knowledge of AI/ML training architectures, distributed training systems, model parallelism strategies, and training performance optimization to effectively define product strategy and make informed technical decisions.

• Training Product Strategy &
  
  Roadmap:
  
  Define and execute training product strategy and roadmap working backwards from customer requirements, produce PRFAQs and PRDs, drive technical alignment across Neuron training libraries and infrastructure, and define reusable building blocks.
• Post-Training, RL & Emerging Workflows: Drive strategy for post‑training workflows including RLHF, DPO, reward modeling, and fine‑tuning at scale; lead the product experience for RL research‑to‑production workflows on Trainium.
• Customer Engagement & Enablement: Work with BD, Solutions Architecture, and GTM teams to engage customers, translate pain points into product requirements, define success metrics, and support enablement for migration and optimization.
• Training AI/ML Ecosystem & Delivery: Define how Neuron supports the training AI/ML ecosystem, integrate with ecosystem tools, track trends, and engage open‑source community and partnership discussions.
• Launch & Go‑to‑Market: Lead end‑to‑end launches for training capabilities, coordinate documentation and field enablement, partner with Marketing and Solutions Architecture to drive awareness, and track adoption metrics.

• 7+ years as a Technical Product Manager.
• Bachelor's degree in computer science, engineering, analytics, mathematics, statistics, IT or equivalent.
• Experience with large‑scale model training workflows and distributed training concepts.
• Familiarity with major AI/ML training frameworks (JAX or PyTorch) and how training libraries interact with them.
• Experience driving product strategy, long‑term roadmap development, and cross‑organizational alignment.
• Excellent written and verbal communication, including executive‑level communication.

• Experience with PyTorch or JAX distributed training.
• Track record of driving developer training libraries and tools.
• Experience with design and scaling of training optimization software (e.g., NeMo, Torch Titan, TRL, VeRL, Max Text, AXLearn, or similar).
• Experience leading RL for research‑to‑production at scale.
• Experience with post‑training workflows including RLHF, DPO, reward modeling, and fine‑tuning.
• Experience with AI/ML training accelerators and hardware, including training performance optimization, profiling, and tooling.
• Experience with distributed training of large‑scale models, including model parallel training techniques (tensor, pipeline, sequence, and expert parallelism).
• Experience working on open‑source and Git Hub‑first developer products with deep customer interactions.
• Track record of driving open standards and AI/ML ecosystem integration for training workflows.
• Experience operating in early‑stage, ambiguous environments with startup‑like velocity.

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Benefits include comprehensive health insurance, 401(k) matching, paid time off, and parental leave. Salary range is USD  –  annually for Cupertino, CA; USD  –  annually for Seattle, WA.