Senior Machine Learning Engineer

Summary

Join ServiceNow as a Machine Learning Engineer and design, develop, and evaluate end-to-end machine learning solutions, focusing on large language models (LLMs). Lead the development of proofs of concept and research prototypes to explore novel AI capabilities. Conduct experiments to assess model behavior and fairness. Generate and curate datasets to optimize model performance. Fine-tune and deploy large-scale models using various techniques. Collaborate with cross-functional teams and contribute to research publications. Define and implement rigorous evaluation protocols, including bias detection and safety metrics. Develop CI/CD pipelines for scalable model deployment. Identify risks in AI applications and contribute to responsible AI initiatives. This role requires strong proficiency in Python and ML frameworks, experience with LLMs, and excellent communication skills.

Requirements

• 5+ years of experience in machine learning, deep learning, or data science, with a track record of applied research or experimentation
• Strong proficiency in Python and ML frameworks such as PyTorch, TensorFlow, HuggingFace Transformers, and NumPy
• Hands-on experience with prompt engineering, model training, evaluation, and optimization for LLMs or foundation models
• Proven experience in applied research, academic publication, technical blogging, or contributions to open-source ML projects
• Familiarity with data-centric AI workflows: synthetic data generation, labelling strategies, and dataset versioning tools
• Deep understanding of AI/ML evaluation strategies, model robustness techniques, and responsible AI practices
• Practical experience deploying models using inference platforms like Triton, ONNX in production environments
• Experience working with MLOps stacks: CI/CD, experiment tracking (e.g., MLflow), Docker, Kubernetes, and distributed training frameworks
• Excellent communication skills with the ability to explain complex ML ideas to non-technical stakeholders and contribute to scientific documentation

Responsibilities

• Design, develop, and evaluate end-to-end machine learning solutions, with a focus on large language models (LLMs), combining engineering rigor and research depth
• Lead the development of PoCs and applied research prototypes to explore novel AI capabilities, model interpretability, and safety strategies
• Conduct cutting-edge experiments to assess model behaviour, generalization, and fairness across diverse datasets and use cases
• Generate and curate synthetic and real-world datasets to optimize model robustness, reliability, and performance
• Fine-tune and deploy large-scale models, incorporating prompt engineering, few-shot learning, and retrieval-augmented techniques
• Collaborate cross-functionally with product, research, and engineering teams to publish white papers, participate in conferences, and contribute to open-source or peer-reviewed ML/AI research
• Define and implement rigorous evaluation protocols, including human-in-the-loop testing, bias detection, and safety metrics
• Develop CI/CD pipelines and containerized workflows for scalable training, evaluation, and deployment of ML solutions in production
• Identify risks in AI applications and contribute to responsible AI initiatives, including transparency, robustness, and compliance frameworks

Preferred Qualifications

• Experience in using AI Productivity tools such as Cursor, Windsurf, etc. is a plus or nice to have
• Experience with methods of training and fine-tuning large language models, such as distillation, supervised fine-tuning, and policy optimization