Senior Machine Learning Engineer

Summary

Join ServiceNow as an AI/ML engineer to contribute to the development and improvement of AI solutions. You will generate and evaluate synthetic data to enhance the robustness and safety of machine learning models, particularly large language models (LLMs). Responsibilities include training and fine-tuning models, designing evaluation metrics, conducting experiments, collaborating with engineering and research teams, and participating in the deployment and continuous improvement of end-to-end AI solutions. You will also contribute to architectural and technology decisions related to AI infrastructure. The role requires proficiency in Python and relevant frameworks, experience in synthetic data generation and model evaluation, and a solid understanding of LLM techniques and AI safety principles. The position offers a flexible work environment.

Requirements

• 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

Responsibilities

• 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