• Lead or co-lead technically complex ML engineering projects from initial customer discussions through implementation and delivery • Design, build, and improve LLM training and post-training pipelines, including data ingestion, preprocessing, fine-tuning, evaluation, and experiment tracking • Implement and optimize evaluation systems for LLMs and multimodal models, including offline benchmarks and task-specific test harnesses • Integrate human-in-the-loop and AI-augmented evaluation signals into model development workflows • Build robust infrastructure and tooling for reproducible experimentation, metrics logging, and regression monitoring • Diagnose model behavior and pipeline failures, including data issues, training instability, metric inconsistencies, and evaluation drift • Collaborate with Language Data Scientists and Applied Research Scientists to translate evaluation frameworks into executable systems • Work closely with customer technical stakeholders to understand goals, constraints, and success criteria; propose and implement technically sound solutions • Contribute to internal research and platform development, including benchmark frameworks, evaluation tooling, and post-training workflow improvements • Contribute to best practices and standards for LLM training, evaluation, and quality assurance across projects • Mentor junior engineers and contribute to technical design reviews, documentation, and engineering rigor across the team