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Forward Deployed Engineer (Model Customization and Optimization)

Amazon· AWS Forward Deployed Engineering
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Hybrid (USA, MA, Boston), Hybrid (USA, WA, Seattle), Hybrid (USA, VA, Arlington), Hybrid (USA, NY, New York), Hybrid (USA, CA, East Palo Alto)$143.7k - $194.4kFull-timePosted yesterday

About the role

Forward Deployed Engineer II on the Custom Model and Optimization team who embeds with strategic customers to build, optimize, and run custom generative AI models in production. Owns end-to-end model workstreams from evaluation and data preparation through training, optimization, and production deployment.

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.
  • 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.

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

Skills

Required:Model customization and optimizationDistributed training and inference systemsMLOps and production deployment

Benefits

  • Base salary with sign-on payments and restricted stock units (RSUs)
  • Comprehensive benefits including health insurance (medical, dental, vision, prescription) and life insurance, EAP, mental health support, flexible spending accounts, adoption and surrogacy reimbursement coverage
  • 401(k) matching
  • Paid time off and parental leave
Last updated Oct 25, 2026. Interested? Apply on the company site.

About Amazon

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