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

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 customers 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 customers 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
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Team
The Custom Model and Optimization team part of the Forward Deployed Engineering team partners with enterprises AI-native startups and foundation model providers to build customize and optimize generative AI models at scale. Our work spans domain foundation models post-training of open-weight models and moving training and inference workloads onto AWS Trainium. As embedded engineers within the AWS Forward Deployed Engineering motion we bring deep model and optimization expertise to customers most strategic AI workloads and we turn what we learn into tooling and patterns that scale across AWS.

10047

- 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

- Bachelors degree in computer science or equivalent
- Experience with large-scale distributed training of LLMs on multi-node accelerator clusters using frameworks such as PyTorch FSDP2 TorchTitan Megatron-LM/Megatron Core NeMo DeepSpeed or NeuronX Distributed
- Experience with LLM post-training including SFT DPO RLHF and RL with verifiable rewards or multi-turn agentic RL using frameworks such as Miles slime verl NeMo-RL OpenRLHF SkyRL or TRL
- Experience optimizing model training or inference for latency throughput or cost using serving engines such as SGLang vLLM or TensorRT-LLM on AWS Trainium or Inferentia (Neuron SDK) on AWS services such as SageMaker AI SageMaker HyperPod or EKS or by writing custom kernels (CUDA Triton NKI)
- Experience deploying and operating ML models in customer production environments working with teams that build and scale end-to-end production systems

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

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process including support for the interview or onboarding process please visit for more information. If the country/region youre applying in isnt listed please contact your Recruiting Partner.

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience qualifications and location. Amazon also offers comprehensive benefits including health insurance (medical dental vision prescription Basic Life & AD&D insurance and option for Supplemental life plans EAP Mental Health Support Medical Advice Line Flexible Spending Accounts Adoption and Surrogacy Reimbursement coverage) 401(k) matching paid time off and parental leave. Learn more about our benefits at MA Boston - 143700.00 - 194400.00 USD annually
USA NY New York - 158100.00 - 213800.00 USD annually
USA VA Arlington - 143700.00 - 194400.00 USD annually
USA WA Seattle - 143700.00 - 194400.00 USD annually