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   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 for  quality degradation and system failures   Design batching, caching, and fallback mechanisms to balance  cost, throughput, and accuracy   Collaborate with Platform teams on  model serving, APIs, and infrastructure scaling   Maintain clear documentation of  annotation strategies, metrics, and known limitations   Qualifications   Education & Experience   MS or PhD in Computer Science, Engineering, Mathematics, or related field   5+ years of experience in  Machine Learning / AI , with focus on:    Large Language Models (LLMs)   Vision-Language Models (VLMs)   Da