Principal Machine Learning Engineer - Model Efficiency Optimization
ABBYYBangalore, India · Posted 3 months agoDescription
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 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
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