Requirements

• Experience in professional, non-internship software development
• Proficiency in at least one modern programming language such as Java, C++, or C# including object-oriented design
• 3+ years of non-internship professional software development experience
• 2+ years experience in Agile, startup, or rapidly scaling environment
• Hands-on experience with generative AI technologies such as large language models (LLMs), vision-language models, or speech models
• Experience with AI tools including evaluation frameworks, agent tooling, retrieval-augmented generation pipelines, and prompt engineering
• Ability to communicate technical concepts effectively to both technical and non-technical audiences

Nice to Haves

• Bachelor's degree in computer science or equivalent
• Experience with full software development lifecycle practices including coding standards, code reviews, source control, 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, NeMo, DeepSpeed, or NeuronX Distributed
• Experience with LLM post-training methods including supervised fine-tuning, direct preference optimization, RLHF, and multi-turn agentic reinforcement learning using frameworks like Miles, slime, verl, NeMo-RL, OpenRLHF, SkyRL, or TRL
• Expertise optimizing model training or inference for latency, throughput, or cost using serving engines like SGLang, vLLM, or TensorRT-LLM on AWS Trainium or Inferentia with tools such as AWS Neuron SDK
• Experience deploying and operating machine learning models in customer production environments and scaling end-to-end production systems

What You'll Be Doing

• Embed directly with strategic customers to understand their business, data, model stack, and operational needs
• Define evaluation criteria and technical plans for model selection, customization, and serving architecture, including up to 25% travel
• Customize and post-train generative AI models through continued pre-training, fine-tuning, preference optimization, and reinforcement learning techniques
• Build data pipelines, synthetic data generation tools, and evaluation frameworks to demonstrate customized model performance improvements
• Design and implement large-scale distributed training and RL pipelines using advanced frameworks
• Develop production inference stacks optimized for throughput, latency, memory, and cost through techniques such as quantization and batching
• Migrate and optimize training and inference workloads on AWS Trainium hardware and profile performance to close accuracy and efficiency gaps
• Codify reusable fine-tuning recipes, benchmarking tools, and optimization patterns to accelerate future projects
• Collaborate with AWS service teams to feed back field learnings and identify expanded opportunities for model customization and optimization

Perks and Benefits

• Work-life harmony with flexible working culture supporting success at work and home
• Inclusive and diverse team culture with employee-led affinity groups and inclusion events
• Access to mentorship, knowledge sharing, and career growth resources
• Opportunity to contribute to leading-edge AI innovation within a global cloud platform trusted by a wide range of customers