Job Opportunities API

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 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 b