Staff AI/ML Software Engineer

Summary

Join ServiceNow's Connected Customer Experience (CCX) team as a Staff AI/ML Software Engineer and build data pipelines, ML models, and secure, scalable, reusable code. Grow the business by bringing internal products to market and personalizing experiences with AI/ML. Implement software empowering internal customers. Develop real-time and batch ML models using various techniques, integrate user behavior signals, and work with LLM technologies. Collaborate with cross-functional teams, optimize algorithms, and monitor model performance. Analyze data, build data models and pipelines, and utilize distributed computing strategies. This role requires significant experience in software development and AI/ML.

Requirements

• 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
• Strong programming skills in Python, Java, SpringBoot, or Scala
• Experience with ML frameworks like TensorFlow, PyTorch, XGBoost, TensorFlow or LightGBM
• Familiarity with information retrieval techniques (BM25, vector search, learning-to-rank)
• 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

• Develop real-time and batch ML models using embeddings, collaborative filtering, and deep learning
• Integrate user behavior signals, session data, and content metadata to optimize relevance
• Work 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
• Collaborate cross-functionally with product, data, and infra teams to deploy experiments and measure impact
• Optimize retrieval, filtering, and ranking algorithms in production search pipelines
• Develop real-time Personalization using query Embeddings for Search Ranking
• Monitor model performance and continuously iterate using A/B testing and offline evaluation metrics
• Analyze various data and building data modeling and pipeline leveraging BigQuery data streaming or Databricks in near real-time
• Distribute computing strategies in Azure, AWS or GCP Cluster, enhancing the parallel processing capabilities