Data Science (AI + Machine Learning) Intern - Master's degree

About Marvell

Marvell’s semiconductor solutions are the essential building blocks of the data infrastructure that connects our world. Across enterprise, cloud and AI, automotive, and carrier architectures, our innovative technology is enabling new possibilities. 

At Marvell, you can affect the arc of individual lives, lift the trajectory of entire industries, and fuel the transformative potential of tomorrow. For those looking to make their mark on purposeful and enduring innovation, above and beyond fleeting trends, Marvell is a place to thrive, learn, and lead. 

Your Team, Your Impact

The Core Complex organization in the Processor Business Unit designs, builds, and integrates the processor, coherent cache, interconnect fabric, and the IO-bridge. The team works across Architecture, RTL, Verification, Physical Design disciplines to deliver high-performance, low-power SoCs for use in wireless infrastructure and networking equipment including servers, switches, routers, secure gateways, firewall, network monitoring, and smartNICs.

What You Can Expect

Algorithm Development and Data Analysis:

• Collaborate with the team to develop and implement AI/ML algorithms.
• Conduct data analysis, leveraging skills in NLP, time-series analysis, and computer vision.

Dataset Mapping and Analysis:

• Map and analyze multiple datasets, with a focus on text-based data.
• Perform competitive landscape analyses for specific targets using acquired datasets.

Programming and Framework Utilization:

• Utilize programming languages such as Python, R, or Java for algorithm implementation.
• Work with machine learning frameworks like TensorFlow or PyTorch to develop and fine-tune models.

Natural Language Processing (NLP) Implementation:

• Apply NLP tools and techniques to enhance language-related tasks within AI projects.

Deep Learning Architecture Understanding:

• Demonstrate understanding of deep learning architectures, particularly transformers.

Infrastructure Development:

• Contribute to building large-scale distributed fine-tuning and training infrastructure.
• Deploy and optimize large language models on GPU instances for efficient computation.

Collaborative Teamwork:

• Collaborate effectively within the team environment on various AI and ML projects.
• Communicate and share insights with team members to enhance project outcomes.

Ethics and Bias Mitigation:

• Consider and apply ethical considerations in AI and machine learning practices.
• Implement bias mitigation strategies in models to ensure fairness and inclusivity.

Documentation and Reporting:

• Document algorithms, methodologies, and findings for future reference.
• Prepare reports and communicate technical ideas to non-technical stakeholders.

Continuous Learning and Skill Development:

• Stay updated on the latest advancements in AI/ML technologies.
• Engage in continuous learning to enhance technical skills and adapt to industry trends.

 

These day-to-day responsibilities reflect a dynamic role that encompasses a range of AI and ML tasks, from algorithm development to ethical considerations and collaborative teamwork.

What We're Looking For

1. Educational Qualification:
  - An undergraduate degree is essential. The preference is someone pursuing a post-graduate degree in fields like Data Science, Mathematics, Computer Science, Engineering, Machine Learning, or Statistics.
2. Relevant Experience:
  - Coursework and project experience in AI/ML algorithm development and data analysis, including specializations such as NLP, time-series analysis, and computer.
  - Experience with mapping and analyzing multiple datasets, especially text-based data, is important. For example, performing competitive landscape analyses for specific targets
3. Technical Skills:
  - Strong proficiency in programming languages such as Python, R, or Java.
  - Knowledge of machine learning frameworks like TensorFlow or PyTorch.
  - Experience with natural language processing (NLP) tools and techniques.
  - Understanding of deep learning architectures, especially transformers, which are commonly used in large language models.
4. Infrastructure Knowledge:
  - Experience in building large-scale distributed fine-tuning and training infrastructure.
  - Experience deploying large language models on GPU instances for efficient computation.
5. Soft Skills:
  - Strong analytical and problem-solving skills.
  - Excellent communication skills to convey complex technical ideas to non-technical stakeholders.
  - Ability to work collaboratively in a team environment.
6. Ethics and Bias Mitigation: