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

Amazon is hiring a Forward Deployed Engineer (Model Customization and Optimization), AWS Forward Deployed Engineering in London, United Kingdom. Level rates it ; you can apply on Amazon.

Level's read on this job

On Level's scale, this forward deployed engineer role is Builds AI ●●●●: the job is building AI systems.
Tools and skills the posting cites include Bedrock, vLLM and PyTorch.
Level classifies it as a mid-level role.
Level lists 21,000+ other open roles at Amazon.
The role was posted in the last few days.

AI in this role

Embeds with strategic customers to build, optimize, and run custom generative AI models and production pipelines on AWS.

bedrockvllmpytorchsagemakermegatron-lmsglangaws-trainium

prompt-engineeringragfine-tuningmachine-learninggenerative-aireinforcement-learningdistributed-trainingmodel-optimization

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 for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.

Inclusive Team Culture
AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do.

Mentorship and Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.

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.

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Basic qualifications

- Experience in professional, non-internship software development
- Experience programming with at least one modern language such as Java, C++, or C# including object-oriented design
- 3+ years of non-internship professional software development experience
- 2+ years of experience working in an Agile, startup, or rapidly scaling environment
- Hands-on experience with generative AI technology, such as LLMs, vision-language models, or speech models
- Experience with AI tools: eval frameworks, agent tooling, RAG pipelines, and prompt engineering
- Experience communicating technical concepts to both technical and non-technical audiences

Preferred qualifications

- Bachelor's degree in computer science or equivalent
- Experience with full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations
- 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 opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice (https://www.amazon.jobs/en/privacy_page) to know more about how we collect, use and transfer the personal data of our candidates.

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 https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

How we rate this

Forward Deployed Engineer (Model Customization and Optimization), AWS Forward Deployed Engineering at Amazon rates 100 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Forward Deployed Engineer (Model Customization and Optimization), AWS Forward Deployed Engineering is an open job at Amazon.
Level rates this role AI Level 4 of 4: Builds AI.
The role is based in GB, London.
The job was posted on October 31, 2026.
Seniority: mid.
Applications are handled by Amazon. Apply on Amazon

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Prompt engineeringRAGFine-tuningMachine learningGenerative AIReinforcement learningDistributed TrainingModel Optimization

Also common in forward deployed engineer jobs at this level

Python AI agents TypeScript Machine learning Google Cloud Azure

Pay

Typical forward deployed engineer job: $180k

This posting doesn't list pay. Use the typical forward deployed engineer figure when pay comes up. See forward deployed engineer jobs

Questions you could be asked

How do you structure and test a prompt to get consistent output from a language model?
How would you design a retrieval step so the model answers from real data instead of guessing?
Walk me through fine-tuning a model: what data did you use, and how did you check the result?
Tell me about a project where machine learning was part of your work. What did you do?
Tell me about a project where generative ai was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: Prompt engineering, RAG, Fine-tuning, Machine learning, and Generative AI. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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