Principal Machine Learning Engineer

📋 Job Overview

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 ABBYY

ABBYY is a company with more than 35 years of experience in the market; 10,000 customers trust ABBYY, including many Fortune 500 ones, with names such as DHL, Johnson & JavaScript, FDA, DMV, PwC, KeyBank, Spotify, and H&R BLOCK. We have modernized the market by creating the low-code/no-code IDP.

Our Machine Learning, Natural Language Processing, Computer Vision Technologies, and a marketplace built with AI, can transform any document in any process; Top Analyst firms recognize ABBYY's leadership, including GABBYY, Everest PEAK Matrix, ISG Intelligent Lens, and NelsonHall, among others.

ABBYY is an Equal Employment employer that values the strength that diversity brings to the workplace. To learn more about our commitment to Diversity and Inclusion, check out the careers section on our website.

🎯 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

• 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
• 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
• 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



📌 Required Qualifications

• 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 track record of contributions to model efficiency (e.g., publications, patents, or industry impact)
• Proven experience optimizing large-scale language and/or vision models for production
• Deep understanding of trade-offs between model quality, size, and inference performance
• Deep expertise in model optimization and compression techniques (e.g., quantization, pruning)
• Strong knowledge of efficient deep learning methodologies
• Expertise in Vision-Language Models (VLMs) and multimodal optimization challenges
• Strong programming skills in Python and deep proficiency with PyTorch or similar frameworks
• Experience with distributed training systems and large-scale experimentation workflows
• Strong evaluation methodology for optimized models, including benchmarking, efficiency profiling, and regression analysis
• Recognized technical authority in model optimization or efficient AI systems
• Proven ability to influence technical direction without formal authority
• Strong track record of applied research → production impact
• Collaborative ability to align cross-functional teams



🎁 Benefits

• Comprehensive medical, dental, and life insurance
• Weekly wellness sessions to support your physical and mental well-being
• A generous paid time off policy
• ABBYY is an Equal Employment employer that values diversity
• Remote and hybrid working options
• Flexible hours across most teams
• Two paid volunteering days off per year
• Paid parental leave in all locations



🛂 Visa & Eligibility