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.

Working 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.

This 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.

Key Responsibilities:

Own the end-to-end training pipeline, including data ingestion, orchestration, checkpointing, and result logging

Execute large-scale experiments with strong emphasis on reproducibility and traceability

Implement and validate new optimization techniques and training objectives in collaboration with senior ML leadership

Continuously improve pipeline efficiency to reduce iteration time while maintaining experiment quality

Evaluation & Benchmarking

Design and maintain comprehensive evaluation and benchmarking frameworks

Define clear success metrics across accuracy, latency, memory usage, and domain coverage

Build automated evaluation pipelines to detect regressions across model checkpoints

Analyze results to identify patterns in model performance and quality trade-offs

Partner with Data teams to ensure improvements in training data translate to measurable gains

Maintain and evolve benchmarking methodologies aligned with industry best practices

Infrastructure & Collaboration

Partner with Platform Engineering on distributed training infrastructure and experiment tracking systems

Develop internal tooling to support model analysis and research workflows

Contribute to team standards around reproducibility, experiment tracking, and documentation

Collaborate with Platform teams to support model deployment, optimization, and serving.

Education & Experience

MS or PhD in Computer Science, Engineering, Mathematics, or related field

5+ years of experience in #MachineLearning, Applied #AI, or related areas

Proven experience training and evaluating large-scale language and/or vision-language models

Strong background in building evaluation frameworks and benchmarking systems

Model optimization or efficient training techniques

Technical Expertise

Deep understanding of model optimization and compression (e.g., quantization, pruning)

Strong proficiency in #Python and #PyTorch, including distributed training frameworks (e.g., #DeepSpeed, FSDP)

Expertise in evaluation methodology and benchmark design

Experience with experiment tracking and reproducibility practices

Familiarity with vision-language model architectures and document AI challenges