Staff Machine Learning Engineer

Summary

Join ServiceNow as a Search and AI Engineer and design and build scalable search ranking, indexing, and AI-based systems. Integrate user behavior signals, session data, and content metadata to optimize relevance. Collaborate with product, data, and infrastructure teams to deploy experiments and measure impact. Drive architectural decisions that align with long-term scalability and maintainability. Leverage your experience with LLM technologies, including generative and embedding techniques, and optimize retrieval, filtering, and ranking algorithms. Monitor model performance and continuously iterate using A/B testing and offline evaluation metrics. Help shape aspects of MLOps and model governance.

Requirements

• Strong analytical and quantitative problem-solving ability
• Experience working with LLM technologies, including developing generative and embedding techniques, modern model architectures, retrieval-augmented generation (RAG), fine tuning / pre-training LLM (including parameter efficient fine-tuning), Deep reinforcement learning and evaluation benchmarks
• Experience in leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving. This may include using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI's potential impact on the function or industry
• 8+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
• 3+ years experience building ML-powered search or recommendation systems
• Mentor and guide engineering teams, fostering a culture of technical excellence and innovation
• Strong programming skills in Python, Java
• Working knowledge of ML frameworks like TensorFlow, PyTorch
• Hands-on experience working on AI search (text, vector and hybrid search)
• Knowledge of embedding models, user/item vectorization, or session-based personalization
• Experience with large-scale distributed systems (e.g., Spark, Kafka, Kubernetes)
• Hands-on experience with real-time ML systems
• Background in NLP, graph neural networks, or sequence modeling
• Experience with A/B testing frameworks and metrics like NDCG, MAP, or CTR

Responsibilities

• Design and build scalable search ranking, indexing and AI-based systems
• Integrate user behavior signals, session data, and content metadata to optimize relevance
• Collaborate cross-functionally with product, data, and infra teams to deploy experiments and measure impact
• Drive & shape architectural decisions that align with long-term scalability and maintainability
• Design aspects of real-time and batch ML models using embeddings, collaborative filtering, and deep learning
• Optimize retrieval, filtering, and ranking algorithms in production search pipelines
• Monitor model performance and continuously iterate using A/B testing and offline evaluation metrics
• Help shape aspect of MLOps and model governance