Job
Description
Company Description It all started in sunny San Diego, California in 2004 when a visionary engineer, Fred Luddy, saw the potential to transform how we work. Fast forward to today — ServiceNow stands as a global market leader, bringing innovative AI-enhanced technology to over 8,100 customers, including 85% of the Fortune 500®. Our intelligent cloud-based platform seamlessly connects people, systems, and processes to empower organizations to find smarter, faster, and better ways to work. But this is just the beginning of our journey. Join us as we pursue our purpose to make the world work better for everyone. Job Description What you get to do in this role: Generate and evaluate synthetic data tailored to improve the robustness, performance, and safety of machine learning models, particularly large language models (LLMs). Train and fine-tune models using curated datasets, optimizing for performance, reliability, and scalability. Design and implement evaluation metrics to rigorously measure and monitor model quality, safety, and effectiveness. Conduct experiments to validate model behavior and improve generalization across diverse use cases. Collaborate with engineering and research teams to identify risks and recommend AI safety mitigation strategies. Participate in the development, deployment, and continuous improvement of end-to-end AI solutions. Contribute to architectural and technology decisions related to AI infrastructure, frameworks, and tooling. Promote modern engineering practices including continuous integration, continuous delivery, and containerized workflows. Qualifications Key qualifications: 5+ years of experience in machine learning, deep learning, and AI systems. Proficiency in Python and frameworks like PyTorch, TensorFlow, and NumPy. Experience in synthetic data generation, model training, and evaluation in real-world environments. Solid understanding of LLM fine-tuning, prompting, and robustness techniques. Knowledge of AI safety principles and experience identifying and mitigating model risks. Hands-on experience deploying and optimizing models using platforms such as Triton Inference Server. Familiarity with CI/CD, automated testing, and container orchestration tools like Docker and Kubernetes. Additional Information Work Personas We approach our distributed world of work with flexibility and trust. Work personas (flexible, remote, or required in office) are categories that are assigned to ServiceNow employees depending on the nature of their work. Learn more here. Equal Opportunity Employer ServiceNow is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, creed, religion, sex, sexual orientation, national origin or nationality, ancestry, age, disability, gender identity or expression, marital status, veteran status, or any other category protected by law. In addition, all qualified applicants with arrest or conviction records will be considered for employment in accordance with legal requirements. Accommodations We strive to create an accessible and inclusive experience for all candidates. If you require a reasonable accommodation to complete any part of the application process, or are unable to use this online application and need an alternative method to apply, please contact globaltalentss@servicenow.com for assistance. Export Control Regulations For positions requiring access to controlled technology subject to export control regulations, including the U.S. Export Administration Regulations (EAR), ServiceNow may be required to obtain export control approval from government authorities for certain individuals. All employment is contingent upon ServiceNow obtaining any export license or other approval that may be required by relevant export control authorities. From Fortune. ©2024 Fortune Media IP Limited. All rights reserved. Used under license. Show more Show less
