Senior Machine Learning Engineer - AI-Assisted Data Annotation

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 – AI-Assisted Data Annotation to own the automated annotation track within ABBYY’s Document AI Data team.  This role sits at the intersection of large model capabilities and production data engineering, leveraging LLMs and vision-language models to generate high-quality training data at scale. You will design and build AI-assisted annotation pipelines, ensuring outputs are accurate, measurable, and reliable for downstream model training.  This is an ideal role for engineers who combine deep model expertise with strong system-building instincts and thrive in fast-moving, experimental environments.  Key Responsibilities  Technical Development & Innovation  Design and implement AI-powered annotation pipelines using large models to generate ground truth labels at scale  Develop and refine prompting strategies, few-shot examples, and fine-tuning approaches to improve accuracy and consistency  Build systems for label verification, confidence scoring, and quality validation  Evaluate which tasks are suitable for automated annotation vs. human review, and define decision criteria  Create evaluation frameworks to benchmark automated annotations against human-labeled data  Continuously improve annotation quality using feedback from human review workflows  Project Ownership & Leadership  Own the automated annotation track end-to-end, from architecture through production monitoring  Drive technical decisions across model selection, pipeline design, and validation strategies  Define integration points with platform infrastructure and model serving systems  Collaborate with Data Operations to design human-in-the-loop workflows for efficient review  Contribute to roadmap planning with Principal-level technical leadership  Infrastructure & Scale  Build and optimize large-scale inference pipelines for processing millions of documents  Implement monitoring and alerting f...