Senior Machine Learning Engineer

As a Senior Machine Learning Engineer (Model Training & Evaluation) , you will own the end-to-end training and evaluation cycle for our document AI models.\nWorking closely with the Principal Machine Learning Engineer, you will transform research direction into reliable, reproducible, and scalable experimentation pipelines, ensuring model improvements are measurable and production ready.\n\nThis role is ideal for engineers who thrive at the intersection of applied #ML research and production-grade engineering, combining deep technical expertise with strong experimental rigor.\n\nKey Responsibilities:\n· Own the end-to-end training pipeline, including data ingestion, orchestration, checkpointing, and result logging\n· Execute large-scale experiments with strong emphasis on reproducibility and traceability\n· Implement and validate new optimization techniques and training objectives in collaboration with senior ML leadership\n· Continuously improve pipeline efficiency to reduce iteration time while maintaining experiment quality\n\nEvaluation & Benchmarking\n· Design and maintain comprehensive evaluation and benchmarking frameworks\n· Define clear success metrics across accuracy, latency, memory usage, and domain coverage\n· Build automated evaluation pipelines to detect regressions across model checkpoints\n· Analyze results to identify patterns in model performance and quality trade-offs\n· Partner with Data teams to ensure improvements in training data translate to measurable gains\n· Maintain and evolve benchmarking methodologies aligned with industry best practices\n\nInfrastructure & Collaboration\n· Partner with Platform Engineering on distributed training infrastructure and experiment tracking systems\n· Develop internal tooling to support model analysis and research workflows\n· Contribute to team standards around reproducibility, experiment tracking, and documentation\n· Collaborate with Platform teams to support model deployment, optimization, and serving.\n\nEducation & Experience\n· MS or PhD in Computer Science, Engineering, Mathematics, or related field\n· 5+ years of experience in #MachineLearning , Applied #AI , or related areas\n· Proven experience training and evaluating large-scale language and/or vision-language models\n· Strong background in building evaluation frameworks and benchmarking systems\n· Model optimization or efficient training techniques\nTechnical Expertise\n· Deep understanding of model optimization and compression (e.g., quantization, pruning)\n· Strong proficiency in #Python and #PyTorch , including distributed training frameworks (e.g., #DeepSpeed , FSDP)\n· Expertise in evaluation methodology and benchmark design\n· Experience with experiment tracking and reproducibility practices\n· Familiarity with vision-language model architectures and document AI challenges