Principal Machine Learning Engineer - Model Efficiency Optimization

ABBYY ABBYY · Enterprise · India · R&D (Engineering)

Seeking a Principal Machine Learning Engineer to lead ABBYY's model optimization strategy for document AI at scale. This role involves defining technical direction from research to production, focusing on building efficient, high-performing models. Responsibilities include establishing evaluation frameworks for quality vs. efficiency trade-offs, leading implementation of optimization pipelines, and collaborating with various teams to ensure optimized models meet performance standards. Requires expertise in model optimization techniques, efficient deep learning, VLMs, and Python/PyTorch.

What you'd actually do

  1. Own the end-to-end technical direction for model efficiency and optimization, from research agenda to production deployment
  2. Define approaches for building efficient, production-ready models optimized for document AI use cases
  3. Establish frameworks for evaluating quality vs. efficiency trade-offs (accuracy, latency, memory footprint)
  4. Lead design and implementation of optimization pipelines, training objectives, and compression techniques
  5. Develop novel optimization approaches tailored to document understanding tasks, including layout and multimodal challenges

Skills

Required

  • MS or PhD in Computer Science, Engineering, Mathematics, or related field
  • 10+ years of experience in Machine Learning / AI
  • Model optimization
  • Efficient deep learning
  • Large-scale model deployment
  • 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
  • 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 driving applied research → production impact
  • Excellent communication skills

Nice to have

  • PhD preferred
  • publications, patents, or industry impact

What the JD emphasized

  • model optimization
  • efficient deep learning
  • large-scale model deployment
  • Vision-Language Models (VLMs)
  • multimodal optimization
  • quality vs. efficiency trade-offs
  • model optimization and compression techniques
  • efficient deep learning methodologies
  • Vision-Language Models (VLMs) and multimodal optimization challenges
  • evaluation methodology for optimized models

Other signals

  • model optimization
  • efficient deep learning
  • large-scale model deployment
  • Vision-Language Models (VLMs)
  • multimodal optimization