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Machine Learning Engr, Principal - Bengaluru, India - Synopsys

Synopsys

Verified Company

Bengaluru, India

1 month ago

Posted by:

Deepika Kaur

beBee Recuiter

Description

46996BR

INDIA
Bangalore, INDIA
Hyderabad

Job Description and Requirements

Job Description and Requirements:

We are searching for an experienced, passionate, and selfdriven individual who possesses both a broad technical strategy and the ability to tackle architectural and modernization challenges.
As a Principal Engineer you will be leading a team that is driving research and operationalization in this area, combining industry best practices and a firstprinciples approach to design and build GenAI and ML infrastructure that will improve Synopsys's AI products. You will be building, leading, and supporting a worldclass team of AI/ML and Platform Engineers to bring and support this innovative research to Synopsys R&D and users all over the world. Your responsibilities will include collaborating with product leadership and core engineering teams, tracking project schedules, communicating risk areas to upper management, fostering innovation, and ensuring the high productivity and career development of your team members.
Duties
Conduct experiments to evaluate model performance, identify areas for improvement, and implement optimizations.
Partner with crossfunctional teams to design and develop scalable solutions that meet business goals.
Communicate complex technical concepts and findings to both technical and nontechnical stakeholders.
Participate in code reviews, testing, and deployment of machine learning models and algorithms.

Required Qualifications

MS/Ph. D with 15+ years' Computer Science, Electrical Engineering, Mathematics, or related field with good publication history would be a good fit for this position.
Proficient in software development practices, including spec'ing, documenting, testing, reviewing, deploying, and monitoring, and are passionate about writing clean, defensible code in an Agile development flow.
5+ years of deep experience in ML/AI, LLMs, RND workflows, practices, and techniques in a production environment.
Ability to communicate a compelling vision and inspire others to deliver on an ambitious and complex roadmap.
Proven familiarity with python, and excellent background in data structures and algorithms.
Good expertise of Probability and Statistics concepts, including Probability, Conditional Probability, Bayes Theorem, Normal Distribution, and Central Limit Theorem.
Sound knowledge of Linear Algebra and Calculus concepts
Sound knowledge of deep learning architectures like Recurrent Neural Networks (RNNs), Long-ShortTerm-Memory models (LSTMs), and Convolutional Neural Networks (CNNs).
Experience with deep learning frameworks like TensorFlow or PyTorch.
Experience with LLMs, Encoder-Decoder Models, and other Generative AI techniques.
Experience with Natural Language Processing (NLP) and Text Generation using Deep Learning.
Excellent problemsolving skills and ability to work autonomously as well as collaboratively in a team environment.
Excellent communication and presentation skills, with the ability to communicate complex technical concepts to both technical and nontechnical stakeholders.
Good expertise with handson experience in data cleansing and modeling for deep learning models in at least one domain (language, image, graphs, etc.)
Experience with cloudbased machine learning platforms such as AWS, GCP, or Azure

Preferred Qualifications

Proven publication record in toptier conferences and journals in the field of Machine Learning or NLP or Generative AI.
Experience with standard machine learning frameworks and tools (HuggingFace Transformers, NumPy, Scikitlearn, Pandas, PyTorch, TensorFlow, etc.) and machine learning cloud infrastructure and accelerators (AWS, Google Cloud, GPUs and TPUs).
Familiarity with supervised and unsupervised learning algorithms like linear regression, logistic regression, random forests, and kmeans.
Prior exposure to AI/ML workflows and tools
Knowledge and/or exposure to cloud computing technologies like containerization platforms (Docker, Kubernetes, microservices)
Broad expertise and understanding of AI, NLP, LLM, and generative AI trends.
Proficiency in advanced concepts and techniques like Proximal Policy Optimization (PPO) and RLHF for building generative models is a big plus.
Experience prototyping, experimenting, and testing with large datasets and training models.

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