2 Dataset Curation Jobs

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&aposs mission Competitive compensation and benefits package Join us in shaping the future of intelligent systems and their applications across diverse scientific domains. Show more Show less

Role & Responsibilities Conduct original research on AI applications, focusing on machine learning algorithms and data-driven methodologies. Design, implement, and evaluate innovative algorithms to solve complex problems in various domains. Collaborate with cross-functional teams to integrate research findings into production systems and prototypes. Analyze and interpret large datasets to extract meaningful insights and validate research hypotheses. Stay current with the latest developments in the AI field and contribute to scholarly publications. Mentor junior researchers and contribute to a collaborative and stimulating research environment. Skills & Qualifications Must-Have Master's degree or PhD in Computer Science, Artificial Intelligence, or related field. Strong knowledge of machine learning and AI algorithms. Proficiency in Python programming for AI applications. Experience in statistical modeling and data analysis techniques. Hands-on experience with natural language processing (NLP) methods. Preferred Familiarity with deep learning frameworks such as TensorFlow or PyTorch. Experience in conducting peer-reviewed research and publications. Excellent problem-solving skills and creativity. Benefits & Culture Highlights Collaborative and innovative work environment that nurtures creativity. Opportunities for professional development and continuous learning. Supportive team culture valuing diversity and inclusion.