# Senior Forward Deployed Engineer (Model Customization and Optimization) at Amazon

Amazon is hiring a Senior Forward Deployed Engineer (Model Customization and Optimization) in Seattle, United States. It pays $168k-$227k a year and Level rates it Builds AI ●●●●; you can [apply on Amazon](https://jobsbylevel.com/go/e24c8a26-115c-465b-aafc-2819ec212f7a?utm_medium=ai-agent).

AI Level 4, AI centrality 98 out of 100. US, WA, Seattle.

## Details

- Company: [Amazon](https://jobsbylevel.com/companies/amazon)
- AI level: AI Level 4 (score 98 out of 100)
- Location: US, WA, Seattle
- Salary: $168k-$227k
- Posted: October 22, 2026
- Apply: https://jobsbylevel.com/go/e24c8a26-115c-465b-aafc-2819ec212f7a?utm_medium=ai-agent

## Prepare for this job

The job page lists 5 likely interview questions for this offer, with a CV checklist: https://jobsbylevel.com/jobs/senior-forward-deployed-engineer-model-customization-and-optimization-at-amazon-a3f71d

## Description

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 customer's 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

The description is cut here. Read the full offer: https://jobsbylevel.com/jobs/senior-forward-deployed-engineer-model-customization-and-optimization-at-amazon-a3f71d

Source: https://jobsbylevel.com/jobs/senior-forward-deployed-engineer-model-customization-and-optimization-at-amazon-a3f71d

## Cite this page

Level. https://jobsbylevel.com/jobs/senior-forward-deployed-engineer-model-customization-and-optimization-at-amazon-a3f71d.

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