Senior Machine Learning Engineer, Synthetic Data & Document Understanding

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

We are seeking a Senior Machine Learning Engineer – Synthetic Data & Document Understanding to own the synthetic data generation track within ABBYY’s Document AI Data team.

This role focuses on building generative pipelines that produce high-quality, diverse, and realistic synthetic training data at scale. You will ensure synthetic data meaningfully improves downstream model performance by maintaining strong alignment with real-world document structures, formats, and statistical properties.

This is an ideal role for engineers who combine deep generative modeling expertise with rigorous data quality evaluation and production engineering skills.

Key Responsibilities

Technical Development & Innovation

• Design and implement pipelines that analyze real documents to inform high-fidelity synthetic data generation
• Build generative systems capable of producing documents across diverse formats, layouts, and domains
• Develop evaluation frameworks to ensure synthetic data maintains distributional fidelity and diversity
• Research and apply generative modeling techniques suited for document AI training
• Identify and mitigate quality issues to ensure synthetic data is effective for downstream model training
• Partner with Modeling teams to measure the impact of synthetic data on model performance

Project Ownership & Leadership