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 HR 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 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 Qualifications Education Experience MS or PhD in Computer Science, Engineering, Mathematics, or related field 5+ years of experience in Machine Learning, 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 Experience with 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) Experience managing large-scale training runs (job scheduling, checkpointing, fault tolerance) Expertise in evaluation methodology and benchmark design Experience with experiment tracking and reproducibility practices Familiarity with vision-language model architectures and document AI challenges ... (truncated, view full listing at source)