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

Amazon Web Services (AWS); Boston (MA); Presencial; USD 144 000 - 194 000; Tempo integral

Resumo da oferta

AWS is seeking a Forward Deployed Engineer II on the Custom Model and Optimization team in the Boston area. You will embed with strategic customers to build, optimize, and run custom generative AI models in production with their engineering teams, owning complete model workstreams from data prep to deployment.

You will design evaluation bars, develop data pipelines, and optimize training and inference using advanced frameworks.

Qualificações

• 3+ years of non-internship software development experience.
• 2+ years of design or architecture experience on scalable systems.
• Hands-on experience with generative AI technology such as LLMs or related models.

Responsabilidades

• Embed within customer ML and engineering teams to translate goals into measurable evaluation bar and technical plan.
• Customize and post-train models including open-weight and foundation models for customer domains.
• Build large-scale training and inference systems with distributed training and RL post-training pipelines.
• Optimize models on AWS chips, migrating to AWS Trainium and tuning for latency and cost.
• Codify reusable fine-tuning recipes and patterns for future deployments and customer benefits.

Conhecimentos

3+ years software

2+ years design/architecture

Programming experience

Generative AI experience

Formação académica

Bachelor's degree in CS or equivalent

Ferramentas

PyTorch FSDP2

Megatron-LM

Miles

verl

NeMo Gym

OpenEnv

SGLang

vLLM

CUDA

Triton

Descrição da oferta de emprego

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.

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.

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

• 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

Preferred Qualifications

• Bachelor's 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 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.

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 https://amazon.jobs/en/benefits.

USA, MA, Boston - 143,700.00 - 194,400.00 USD annually

USA, NY, New York - 158,100.00 - 213,800.00 USD annually

USA, VA, Arlington - 143,700.00 - 194,400.00 USD annually

USA, WA, Seattle - 143,700.00 - 194,400.00 USD annually

Company

Amazon Web Services, Inc.

Job ID: A10585967