Applied AI Scientist

PyjamaHR; Kalaburagi; On-site; INR 1,200,000 - 2,200,000; Full time

Benefits offered by this job

Access to high-performance computing resources

Collaborative, intellectually stimulating environment

Opportunities for growth

Autonomy to explore novel ideas

Competitive salary and benefits

Job summary

PyjamaHR is seeking an Applied AI Researcher to join their advanced research team in the deep-tech and AI research industry. The role involves designing multi-agent systems, optimizing AI models, and collaborating with various teams. Ideal candidates will possess a Master's or PhD in relevant fields and have a strong research background in AI.

Qualifications

• 4+ years of experience in applied AI research or equivalent industry R&D experience.
• Strong foundations in optimization, probability, and linear algebra.
• Familiarity with prompting strategies like Chain of Thought.

Responsibilities

• Architect and implement novel multi-agent systems enabling advanced problem-solving.
• Curate and manage high-quality datasets for AI training and evaluation.
• Collaborate cross-functionally to translate research into production-ready solutions.

Skills

Research

Design

Multi-agent Systems

Models

Education

Master’s or PhD in Computer Science, AI, Machine Learning

Tools

Python

PyTorch

JAX

Job description

6 days ago Be among the first 25 applicants

This range is provided by PyjamaHR. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

Base pay range

We’re hiring an Applied AI Researcher in the deep-tech and AI research industry.

We are seeking a highly skilled and motivated professional to join our advanced research team. In this role, you will design multi-agent architectures, develop domain-specific scaffolding techniques, and build evaluation frameworks for next-generation AI systems. You’ll work at the frontier of applied AI—combining LLMs, reinforcement learning, and multi-agent systems to build scalable and meaningful solutions.

Key Responsibilities

• Architect and implement novel multi-agent systems that enable advanced problem-solving and collaboration
• Design domain-specific scaffolding techniques to tailor AI behavior to complex, real-world domains
• Curate and manage high-quality datasets for training and evaluation of AI models across scientific and industrial use cases
• Establish and iterate on robust evaluation frameworks to measure performance, alignment, and robustness
• Research and apply reinforcement learning techniques, including RLHF, DPO, GRPO, and others
• Explore post-training optimization, fine-tuning, and domain adaptation methods
• Collaborate cross-functionally with engineering and product teams to translate research into production-ready solutions
• Stay abreast of cutting-edge developments in AI and contribute to internal and external research communities
• Document and communicate findings through technical reports, presentations, and publications
  
  

Required Qualifications

• Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field
• 4+ years of experience in applied AI research or equivalent industry R&D experience
• Strong foundations in optimization, probability, and linear algebra
• Expertise in Python and frameworks like PyTorch or JAX
• Experience with RL and post-training methods (e.g., SFT, DPO, RLHF)
• Proficiency in building and aligning small language models (SLMs), including reasoning-specific models
• Familiarity with prompting strategies like Chain of Thought and dataset design for reasoning tasks
• Deep understanding of multi-agent systems and distributed training (multi-GPU/multi-node)
• Experience in designing evaluation metrics and performance analysis methodologies
  
  

Preferred Experience

• Publications in leading ML conferences (NeurIPS, ICLR, ICML, AAMAS, etc.)
• Experience applying AI to scientific domains like drug discovery, chip design, or materials science
• Exposure to multimodal models and VLMs
• Experience with open-ended systems and emergent behavior in agent-based learning
• Background in computational science (chemistry, physics, EE, or applied math)
• Familiarity with MLOps, Kubernetes, Ray, Hydra, and MLflow
• Experience with domain adaptation, interpretability, and model optimization for deployment
• Contributions to open-source AI projects
• Expertise in building GPU-accelerated pipelines and optimizing inference at scale
  
  

What We Offer

• Work on high-impact, frontier research with real-world applications
• Access to high-performance computing resources
• A collaborative, intellectually stimulating environment
• Autonomy to explore novel ideas aligned with our mission
• Competitive salary, benefits, and opportunities for growth
  
  

Skills: Research,Design,Multi-agent Systems,Models

Seniority level

• Seniority level

  Entry level

Employment type

• Employment type

  Full-time

Job function

• Job function

  Engineering and Information Technology

• Industries

  Software Development

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