We are seeking a driven Deep Learning Staff Engineer who thrives working in a fast-paced development environment. You will be part of a team that creates deep learning processes from the concept stage through R and D and to productization. The role can be summed up as building and deploying cutting-edge generalized deep learning architectures that can solve complex business problems.

Responsibilities

• Build state-of-the-art models to solve complex business problems.
• Lead the research initiatives for the Deep learning projects to continuously experiment and incorporate new advancements in the field into different system architectures, streamlining engineering efforts
• Create highly scalable frameworks for all stages of the DL model lifecycle including data curation, synthetic data creation, training, and inference.
• Evaluate the current procedures to identify shortcomings and take steps to address them
• Drive innovative ideas for modeling techniques and performance enhancements.
• Collaborate with infrastructure teams to develop tools and interfaces for quick benchmarking, model analysis and optimization, hyper-parameter tuning, and monitoring
• Take care of different data engineering tasks like defining data requirements, labeling, augmenting, etc, modeling tasks like training deep learning models, defining evaluation metrics, etc., and deployment tasks like converting prototyped code into production code, setting up a cloud environment to deploy the model, etc.
  
  

Requirements

• Bachelor's degree in engineering, Computer science, Information Systems, or a related field.
• A master's degree is a plus.
• 8+ years of experience in Deep Learning.
• Experience in Python, CUDA, AWS, tensorflow/pytorch, etc.
• Very good communication skills.
• Strong analytical and problem-solving skills.
• Experience in NLP (natural language processing), multi-modal AI like VLM (Vision-Language Models) and LLM(Large language models)
• Very strong research background.
• Experience with DL frameworks, building and deploying systems.
• Have previously shipped something of significance, either implemented some paper or made significant changes in an existing architecture, etc.
  
  

This job was posted by Kratika Gupta from Nanonets.