Principal Machine Learning Engineer - Model Efficiency Optimization

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 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

Technical Expertise

• 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

Leadership & Communication

• Recognized technical authority in model optimization or efficient AI systems

• Proven ability to influence technical direction without formal authority

• Strong track record of driving applied research production impact

• Excellent communication skills, with the ability to clearly articulate complex trade-offs

• Collaborative mindset with the ability to align cross-functional teams

Here are some of our local benefits:

• Comprehensive medical, accidental, and life insurance

• Weekly wellness sessions to support your physical and mental well-being

• A generous paid time off policy