Senior Machine Learning Engineer, Model Training & Evaluation

Join ABBYY and be part of a team that celebrates your unique work style. With flexible work options, a supportive team, and rewards that reflect your value, you can focus on what matters most – driving your growth, while fueling ours. Our commitment to respect, transparency, and simplicity means you can trust us to always choose to do the right thing. As a trusted partner for purpose-built AI and intelligent automation, we solve highly complex problems for our enterprise customers and put their information to work to transform the way they do business. Over 10,000 customers trust ABBYY, including many Fortune 500 ones. You will work on further developing a portfolio already containing client names such as DHL, Johnson & Johnson, FDA, DMV, PwC, KeyBank, Spotify, and H&R BLOCK.About the Role  As a Senior Machine Learning Engineer (Model Training & Evaluation) at ABBYY, 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  Training Pipeline & Experimentation  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  Investigate training instabilities, loss anomalies, and performance gaps, providing structured analysis and hypotheses  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  Manage compute resources across parallel experiments, balancing throughput and cost efficiency  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 i...