Staff Machine Learning Research Scientist, Generative AI

WerQ AI is leading the charge in transformative workplace technology, and we are excited to invite a talented Staff Machine Learning Research Scientist specializing in Generative AI to join our innovative team. In this role, you will drive advanced research projects focused on generative models, developing cutting-edge AI solutions that enhance creativity and productivity in various applications. Your expertise will enable you to push the boundaries of AI, exploring novel approaches and methodologies. You will work collaboratively with cross-functional teams to design and implement scalable AI systems while mentoring junior researchers and contributing to the development of proprietary technology. If you are passionate about generative AI and thrive in an environment that encourages creativity and technical excellence, we want to hear from you!

Responsibilities

• Lead research initiatives in generative AI, developing innovative algorithms and models that solve complex problems.
• Design experiments and conduct analyses to evaluate model performance and inform further research directions.
• Collaborate with product teams to integrate generative AI solutions into real-world applications and workflows.
• Mentor and guide junior researchers, fostering a culture of knowledge sharing and innovation.
• Publish research findings in top-tier conferences and journals, engaging with the academic and industry communities.
• Stay ahead of industry trends, emerging technologies, and research advancements to keep our AI initiatives competitive.
• Work closely with data engineering teams to ensure the availability and quality of datasets for training and evaluation.

Requirements

• PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field with a focus on generative models.
• 5+ years of research experience in machine learning, with a strong track record in generative AI technologies (e.g., GANs, VAEs).
• Proficiency in programming languages such as Python, along with experience in ML frameworks (e.g., TensorFlow, PyTorch).
• Deep understanding of probabilistic modeling, statistical analysis, and algorithm development.
• Experience with large-scale data processing and cloud technologies for model training and deployment (e.g., AWS, Google Cloud).
• Demonstrated ability to lead research projects and collaborate effectively with diverse teams.
• Strong publication record in reputable conferences/journals and ability to communicate complex ideas clearly.