Machine Learning Engineer, Advertising & Marketing Performance Intelligence

What you’ll do: Work with Data and Applied Scientists to process structured and unstructured data, scaling ML and LLM infrastructure while optimizinginfrastructure costs , GPU utilization , memory management, and production training workflows (including techniques such as optimizer state offloading and massive parallelization).Build and deliver reusable technical assets that enable faster adoption ofML, optimization, and GenAIacross different science initiatives. Design and maintainproduction-grade, large-scale distributed trainingsystems for ML, Causal, GenAI, and multi-modal foundation model workloads. OptimizeAWS AI/ML infrastructurefor training efficiency, including GPU utilization, latency, costs, and fine-tuning on massive datasets. Create monitoring and debugging tooling to improve reliability and performance of training workflows, including support for piloting LLMs and diagnosing system issues. Partner with Engineers, Data, and Applied Scientists to evaluate design approaches, prototype new GenAI and ML models, assess technical feasibility, and resolve complex problems. Basic qualifications3+ yearsof non-internship professional software development experience.2+ yearsof non-internship design or architecture experience for new and existing systems, including design patterns, reliability, and scaling. Experience programming with at least one software programming language. Experience in machine learning, data mining, information retrieval, statistics, or natural language processing. Technologies you may work with Machine Learning, large language models (LLMs), and large quantitative models (LQMs)AI/ML workflowsAWS Bedrock, SageMakerAgentic AI (RAG, agentic architectures, vector databases), Amazon QContainerized deployments, Hugging Face, LangChainGuardrail implementations Foundational models such as Qwen, Anthropic’s Claude, MistralRCTsCompensation and location

Location: Seattle, WA (onsite)

Salary: USD 143,700 - 194,400 per year