Principal Machine Learning Engineer - Model Efficiency Optimization

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