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  Principal Machine Learning Engineer – Model Efficiency & Optimization  to serve as the technical anchor for ABBYY’s model optimization strategy.   This is a  senior individual contributor role  for a deep domain expert who will define how ABBYY builds  efficient, high-performing, production-ready models  for document AI at scale. You will set technical direction from  research exploration through production deployment , combining strong theoretical expertise with hands-on implementation.   Key Responsibilities   Research Direction & Technical Strategy   Own the end-to-end technical direction for  model efficiency and optimization , from research agenda to production deployment   Define approaches for building  efficient, production-ready models  optimized for document AI use cases   Establish frameworks for evaluating  quality vs. efficiency trade-offs  (accuracy, latency, memory footprint)   Set standards for what constitutes a successful optimized model across document understanding benchmarks   Evaluate and adopt emerging techniques in  model optimization and compression   Influence modeling strategy across teams by integrating  efficiency-first thinking  into model development     Hands-on Implementation & Experimentation   Lead design and implementation of  optimization pipelines, training objectives, and compression techniques   Run large-scale experiments and analyze  training dynamics, instabilities, and capability gaps   Develop novel optimization approaches tailored to  document understanding tasks , including layout and multimodal challenges   Diagnose and resolve failure modes such as  quality degradation and generalization gaps   Prototype and validate new techniques before scaling to production training runs     Cross-Functional Collaboration   Partner with the  Document AI Data team  to define training data requirements for optimized models   Collaborate with Platform teams on  distributed training infrastructure, experiment tracking, and compute strategy   Work closely with Modeling teams to ensure optimized models meet  quality and performance standards   Communicate technical trade-offs, findings, and recommendations clearly to  engineering, product, and leadership stakeholders   Qualifications Education & Experience   MS or PhD in Computer Science, Engineering, Mathematics, or related field ( PhD preferred )   10+ years of experience in  Machine Learning / AI , with focus on:    Model optimization   Efficient deep learning   Large-scale model deployment   Demonstrated&nbsp