Embedded AI Model Engineer II

Amazon Web Services (AWS); Seattle (WA); On-site; USD 144,000 - 194,000; Full time

Job summary

Amazon Web Services, Inc. is seeking a Forward Deployed Engineer II to embed with strategic customers and lead end-to-end model workstreams—from evaluation design through training, tuning, and production deployment—on a range of generative AI systems in production.

You will collaborate with customer engineering teams, design distributed training and inference pipelines, optimize deployments on AWS Trainium, and codify reusable fine-tuning patterns for future engagements.

Qualifications

• 3+ years of non-internship professional software development experience.
• 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience.
• 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 the customer's business, data, model stack, and operational constraints. Translate ambiguous goals into a measurable evaluation bar and a technical plan for your workstream such as model selection, customization approach, and serving architecture. Requires up to 25% travel.
• Customize and post-train models. Adapt open-weight and foundation models to customer domains through continued pre-training, supervised fine-tuning, preference optimization, and reinforcement learning (including RLHF, RL with verifiable rewards, and multi-turn agentic RL). Build the data pipelines, synthetic data generation, and evaluation harnesses that prove a customized model out performs the baseline on the customer's own quality, latency, and cost bar. Apply these techniques to high-value patterns such as coding agents, domain foundation models, and multi-adapter serving.
• Build large-scale training and inference systems. Design and implement distributed training and RL post-training pipelines using frameworks such as PyTorch FSDP2, Megatron-LM, Miles, and verl. Build RL interaction harnesses using NeMo Gym or OpenEnv to connect agent workflows, tools, environments, and reward functions to training. Develop production inference stacks with engines such as SGLang and vLLM, and optimize throughput, latency, memory footprint, and cost per token through quantization, parallelism, speculative decoding, and batching.
• Optimize models on AWS chips. Migrate and tune training and inference workloads for AWS Trainium using the AWS Neuron SDK. Profile performance, close accuracy-parity gaps against GPU baselines, and develop custom kernels where needed. Benchmark rigorously and turn results into decisions customers can act on.
• Accelerate impact beyond the engagement. Codify reusable fine-tuning recipes, evaluation frameworks, benchmarking tools, and optimization patterns that accelerate future deployments. Feed field learnings back to AWS service teams, including Amazon Bedrock, Amazon SageMaker AI, and AWS Neuron. Identify opportunities to expand model customization and optimization within your customer engagement.

Skills

Software development

System design

Programming languages

Generative AI

Education

Bachelor's degree in CS

Tools

PyTorch FSDP2

Megatron-LM

DeepSpeed

NeMo

vLLM

TensorRT-LLM

TorchTitan

Miles

verl

NeMo-RL

OpenRLHF

TRL

SGLang

OpenEnv

Job description

Amazon Web Services, Inc. is seeking a Forward Deployed Engineer II to embed with strategic customers and lead end-to-end model workstreams—from evaluation design through training, tuning, and production deployment—on a range of generative AI systems in production.

You will collaborate with customer engineering teams, design distributed training and inference pipelines, optimize deployments on AWS Trainium, and codify reusable fine-tuning patterns for future engagements.