# Forward Deployed Engineer (Model Customization and Optimization), AWS Forward Deployed Engineering at Amazon

Amazon is hiring a Forward Deployed Engineer (Model Customization and Optimization), AWS Forward Deployed Engineering in Seattle, United States. It pays $144k-$194k a year and Level rates it Builds AI ●●●●; you can [apply on Amazon](https://jobsbylevel.com/go/12893423-5d79-4a00-9231-a49cbd018ae7?utm_medium=ai-agent).

AI Level 4, AI centrality 99 out of 100. US, WA, Seattle.

## Details

- Company: [Amazon](https://jobsbylevel.com/companies/amazon)
- AI level: AI Level 4 (score 99 out of 100)
- Location: US, WA, Seattle
- Salary: $144k-$194k
- Posted: October 23, 2026
- Apply: https://jobsbylevel.com/go/12893423-5d79-4a00-9231-a49cbd018ae7?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=forward-deployed-engineer-model-customization-and-optimization-aws-forward-deployed-engineering-at-amazon-2db4bb

## Prepare for this job

The job page lists 5 likely interview questions for this offer, with a CV checklist: https://jobsbylevel.com/jobs/forward-deployed-engineer-model-customization-and-optimization-aws-forward-deployed-engineering-at-amazon-2db4bb

## Description

As a Forward Deployed Engineer II (FDE II) on the Custom Model and Optimization team, you embed directly with strategic customers to build, optimize, and run custom generative AI models in production alongside their engineering teams. You own complete model workstreams end-to-end from defining the evaluation bar and preparing data, through training and inference optimization, to production deployment. FDE IIs on this team works at the intersection of models and systems. They customize state-of-the-art open-weight and foundation models for new domains, languages, and modalities; migrate and optimize training and inference workloads on AWS Trainium and operate those models as production services. They are autonomous contributors who take on projects where the business goal is clear, but the path is not. They make design trade-offs with appropriate guidance, limit short-term workarounds, coach others on producing high-quality code, and train new team members. Key job 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. About the team ABOUT AWS: Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive

The description is cut here. Read the full offer: https://jobsbylevel.com/jobs/forward-deployed-engineer-model-customization-and-optimization-aws-forward-deployed-engineering-at-amazon-2db4bb

Source: https://jobsbylevel.com/jobs/forward-deployed-engineer-model-customization-and-optimization-aws-forward-deployed-engineering-at-amazon-2db4bb

## Cite this page

Level. https://jobsbylevel.com/jobs/forward-deployed-engineer-model-customization-and-optimization-aws-forward-deployed-engineering-at-amazon-2db4bb.

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