COGNIZANT AI LABS

Cognizant works with an incredible diversity of organizations across the globe, using AI to improve decision-making, robustness, forecasting, and growth at every level of operation.
Within Cognizant, Cognizant AI Labs serves as the center of excellence for pioneering AI research. The team works to develop novel approaches to solve both fundamental scientific problems and challenges from real-world applications.
The work done by Cognizant AI Labs serves to inspire and catalyze real-world applications implemented by Cognizant, and reciprocally, real-world challenges encountered in Cognizant’s diverse ecosystem of applications serve to inspire foundational research at Cognizant AI Labs.

QUALIFICATIONS:

• Should have a PhD in Computer Science or another technical field.
• Passion for AI research and AI for Good.
• 3+ years of experience in AI or ML research.
• Publications at venues such as ICLR, NeurIPS, GECCO, ALife, AAAI, IJCAI, ICML, etc.
• Strong implementation skills.
• Experience with LLMs.
• Strong problem-solving and analytic skills.
• Strong attention to detail and ability to work independently.
• Excellent verbal and written communication skills.
  

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YOUR ROLE:

As a research scientist, you will work with the Cognizant AI Labs research team to develop novel approaches to solving fundamental scientific problems and challenges from real-world applications, using core technologies such as LLMs, evolutionary algorithms, and other machine learning and AI techniques. With an explicit focus on AI for Good applications alongside basic research, the team envisions a world where AI systems are safe, robust, sustainable, long-lived, and inspiring. The team’s current research is focused around areas including, but not limited to:

• AI for decision-making
• AI orchestration
• Trustworthy AI
• Open-ended AI
• Sequential/time-series domains
• Multi-agent systems
• ALife
• LLMs
• Evolutionary computation

KEY RESPONSIBILITIES:

• Work with the Advanced AI Labs research team to develop original ideas that can contribute to the AI community
• Design experiments and evaluation methodologies for testing these ideas
• Implement novel algorithms and evaluation frameworks
• Manage experiments, analyze results, and iterate rapidly
• Communicate ideas and results to a larger audience
• Publish papers based on this work
• Advise AI engineers on the development of practical applications