Applied AI Researcher

About the Role
QpiAI works at the intersection of AI and Quantum Computing, developing groundbreaking solutions to tackle some of the most complex challenges across various industries. Our team is dedicated to pushing the boundaries of innovation and delivering exceptional results to our enterprise clients, driving significant impact.
We are seeking an exceptional Applied AI Researcher to join our innovative research team. This role will focus on advancing our AI capabilities through rigorous experimentation, multi-agent systems design, domain-specific scaffolding, and evaluation methodology development. Through this role you will get the opportunity to develop Compound AI systems impacting areas like Drug Discovery, Decision Intelligence, Chip Design , Quantum Computing etc.
Key Responsibilities
Design and implement novel multi-agent architectures that enable complex problem-solving and collaboration
Develop domain-specific scaffolding techniques to guide AI systems in specialized environments
Create and curate high-quality datasets for training and evaluating AI systems across various domains
Establish comprehensive evaluation frameworks to measure system performance, robustness, and alignment
Research and implement reinforcement learning approaches for agent behavior optimization
Develop techniques for post-training optimization and domain adaptation
Collaborate with cross-functional teams to translate research findings into practical applications
Stay current with the latest advancements in AI research and contribute to the research community
Document methodologies, findings, and best practices for internal knowledge sharing
Qualifications
Advanced degree (MS or PhD) in Computer Science, Machine Learning, AI, or related technical field
4+ years of experience in applied AI research
Strong mathematical foundations in optimization, probability theory, and linear algebra
Extensive expertise in Python programming and software development
Strong proficiency in PyTorch or JAX for implementing and scaling AI models
Experience in curating datasets, pre-training, and aligning multimodal small language models
Demonstrated experience with various reinforcement learning post-training methods (RLHF, DPO, GRPO, SFT, etc.)
Experience with prompting methods like Chain of Thought and curating Chain of Thought datasets
Experience training small reasoning models for specific applications
Familiarity with various alignment methods and value learning techniques
Experience with multi-agent systems design and implementation
Knowledge of distributed training on multi-node, multi-GPU clusters
Experience with dataset curation, cleaning, and augmentation for diverse AI tasks
Strong understanding of model evaluation metrics and methodology development
Excellent communication skills and ability to present complex technical concepts clearly
Preferred Experience
Publication record in top-tier AI/ML conferences (NeurIPS, ICLR, AAMAS, ICML)
Experience applying AI to scientific domains such as drug discovery, materials science, chip design, or mathematical reasoning
Experience working with vision-language models and multimodal architectures
Demonstrated success in developing efficient small language models for specialized tasks
Research experience in open-endedness, including emergent behaviors, auto-curriculum learning, or open-world agent systems for design of experiments, hypothesis generation.
Background in computational chemistry, physics, electrical engineering, or mathematics
Experience with Kubernetes and containerized workflow orchestration
Familiarity with ML infrastructure tools like Ray, Hydra, or MLflow
Knowledge of interpretability methods and system analysis approaches
Experience with domain adaptation for specialized scientific applications
Open-source contributions to AI research or tooling
Experience in developing custom GPU accelerated pipelines.
Experience optimizing large-scale models for inference and deployment.
What We Offer
Opportunity to work on cutting-edge AI research with real-world impact
Access to substantial computational resources for large-scale experimentation
Collaborative environment with top researchers and engineers
Flexibility to pursue novel research directions within the company's mission
Competitive compensation and benefits package
Join us in shaping the future of intelligent systems and their applications across diverse scientific domains.

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