Senior Forward Deployed Engineer (Model Customization and Optimization)

As a Senior Forward Deployed Engineer (Senior FDE) on the Custom Model and Optimization team you are the technical lead embedded inside a strategic customer accountable for building optimizing and running the custom generative AI models at the heart of their business. You set the technical direction for model customization and optimization engagements deciding what to build how to measure it and how it reaches production. You combine the technical depth of an AWS senior engineer with the urgency of a startup builder using your judgment and ownership to deliver measurable business outcomes in quality latency and cost.

Senior FDEs work on the hardest problems where models meet systems: building domain foundation models post-training frontier-scale open-weight models with reinforcement learning and migrating large training and inference workloads onto AWS Trainium. They take on problems where the business problem itself is ambiguous define the approach and break it into workstreams others can own. They make long-term architectural trade-offs balance customer urgency against sustainable design and are trusted by customer engineering leadership as a technical peer. They raise the bar for the team through design reviews code reviews mentorship and hiring and they shape how AWS builds.

Key job responsibilities
Lead the technical engagement: Embed with the customer as the technical lead for their model customization and optimization program spanning multiple workstreams. Partner with customer engineering and product leadership to frame the business problem define success metrics and the evaluation bar and set technical roadmap that moves from proof of value to production. Communicate trade-offs risks and results clearly to executives and engineers alike. Requires up to 25% travel.

Define the customization strategy: Decide the right path to the customers quality latency and cost targets prompting distillation supervised fine-tuning preference optimization reinforcement learning (including RL with verifiable rewards and multi-turn agentic RL) continued pre-training task-specific adapters or building a domain foundation model. Own the data strategy synthetic data generation and evaluation methodology that make those decisions defensible and drive them to production for patterns such as coding agents vertical foundation models and multi-adapter serving.

Architect training and inference systems at scale: Design distributed training and RL post-training systems for large dense and mixture-of-experts models using frameworks such as PyTorch FSDP2 Megatron-LM Miles and verl and production inference architectures using engines such as SGLang and vLLM. Architect RL interaction harnesses using frameworks such as NeMo Gym and OpenEnv defining how agent workflows tools environments and reward functions connect to training and evaluation. Set parallelism precision and serving strategies that balance throughput latency memory reliability and cost per token and ensure the designs hold up under production load.

Lead optimization on AWS AI chips: Lead migrations of training and inference workloads to AWS Trainium using the AWS Neuron SDK. Diagnose performance and accuracy-parity gaps against GPU baselines direct kernel-level optimization where it matters and work with AWS Neuron and service teams to close capability gaps. Turn benchmark evidence into recommendations that shape customer infrastructure and capacity decisions.

Lead mentor and raise the bar: Provide technical leadership to a team of FDEs and scientists on the engagement breaking ambiguous problems into well-scoped workstreams and unblocking others. Mentor FDE IIs on model systems design and production ownership. Lead design reviews provide high-judgment code review and actively participate in hiring and interviewing.

Multiply impact across AWS: Identify patterns across engagements and turn them into reusable fine-tuning recipes evaluation frameworks benchmarking tools and reference architectures adopted by other teams. Influence the roadmaps of AWS services including Amazon Bedrock Amazon SageMaker AI and AWS Neuron with field evidence. Identify and shape opportunities to expand model customization and optimization within your customer and across similar customers.

Oversee engagement execution: Own the delivery plan set milestones track progress against deadlines and ensure the quality and completeness of all deliverables. Hold yourself and the team accountable for commitments made to the customer escalate risks early and adjust scope or resources when timelines are at risk.

About the team
ABOUT AWS:

Diverse Experiences
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Team
The Custom Models & Optimization team part of the AWS 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 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.

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- 5 years of non-internship professional software development experience
- 5 years of programming with at least one software programming language experience
- 5 years of leading design or architecture (design patterns reliability and scaling) of new and existing systems experience
- Experience as a mentor tech lead or leading an engineering team
- Hands-on experience training fine-tuning or optimizing deep learning models
- Experience communicating technical concepts to both technical and non-technical audiences including senior leadership

- Bachelors degree in computer science or equivalent
- 2 years of experience deploying and operating ML models in customer production environments working with teams that build and scale end-to-end production systems
- Experience defining evaluation strategy and building evaluation frameworks or benchmarks for generative models
- Experience leading optimization of model training or inference for latency throughput or cost using serving engines such as SGLang vLLM or TensorRT-LLM on AWS Trainium (Neuron SDK) on AWS services such as SageMaker AI SageMaker HyperPod or EKS or by writing custom kernels (CUDA Triton NKI)
- Experience leading LLM post-training efforts 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 leading large-scale distributed training of LLMs including mixture-of-experts models on multi-node accelerator clusters using frameworks such as PyTorch FSDP2 TorchTitan Megatron-LM/Megatron Core NeMo DeepSpeed or NeuronX Distributed

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

Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees supervisors and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees supervisors and staff to ensure exceptional customer service; and follow all federal state and local laws and Company policies. Criminal history may have a direct adverse and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above as well as the abilities to adhere to company policies exercise sound judgment effectively manage stress and work safely and respectfully with others exhibit trustworthiness and professionalism and safeguard business operations and the Companys reputation. Pursuant to the Los Angeles County Fair Chance Ordinance we will consider for employment qualified applicants with arrest and conviction records.

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 - 168100.00 - 227400.00 USD annually
USA NY New York - 184900.00 - 250200.00 USD annually
USA VA Arlington - 168100.00 - 227400.00 USD annually
USA WA Seattle - 168100.00 - 227400.00 USD annually