Intelligence Researcher

QpiAI works at the intersection of AI and Quantum Computing, developing groundbreaking solutions to tackle some of the most complex challenges across various industries. 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.

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

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

~ 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

~ 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.

Opportunity to work on cutting-edge AI research with real-world impact

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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