Embedded GenAI Engineer - Custom Models & Optimization

Amazon Inc.; Arlington (VA); On-site; USD 144,000 - 194,000; Full time

Job summary

Amazon Web Services (AWS) seeks a Forward Deployed Engineer II on the Custom Model and Optimization team in Arlington, VA. You embed with strategic customers to build, optimize, and run custom generative AI models in production alongside their engineers.

You own end-to-end model workstreams—from evaluating data, training, and inference optimization to production deployment—driving high‑impact AI deployments at customer sites.

Qualifications

• 3+ years non‑internship software development
• 2+ years design/architecture (design patterns, reliability and scaling) of new and existing systems
• Experience programming with at least one software programming language
• Hands‑on experience with generative AI technology, such as LLMs, vision‑language models, or speech models

Responsibilities

• Embed within customer ML and engineering teams: understand customer business, data, model stack, and constraints; translate goals into a measurable evaluation bar and a technical plan; travel up to 25%
• Customize and post-train models: adapt open-weight and foundation models to customer domains through continued pre-training, supervisor fine‑tuning, preference optimization, RL techniques; build data pipelines, synthetic data, and evaluation harnesses
• Build large‑scale training and inference systems: design distributed training and RL post‑training pipelines; develop production inference stacks and optimize throughput, latency, memory, and cost per token
• Migrate and tune training and inference workloads for AWS Trainium using AWS Neuron SDK; benchmark and close accuracy gaps against GPU baselines

Job description

Amazon Web Services (AWS) seeks a Forward Deployed Engineer II on the Custom Model and Optimization team in Arlington, VA. You embed with strategic customers to build, optimize, and run custom generative AI models in production alongside their engineers.

You own end-to-end model workstreams—from evaluating data, training, and inference optimization to production deployment—driving high‑impact AI deployments at customer sites.