Staff AI/ML Software Engineer
| Company | ServiceNow |
|---|---|
| Location | Atlanta, GA, USA |
| Salary | $Not Provided – $Not Provided |
| Type | Full-Time |
| Degrees | |
| Experience Level | Senior, Expert or higher |
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
- 3+ years experience building ML-powered search or recommendation systems.
- Strong programming skills in Python, Java, SpringBoot or Scala.
- Experience with ML frameworks like TensorFlow, PyTorch, XGBoost, TensorFlow or LightGBM.
- 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.
- Hands-on experience working on AI search (text, vector and hybrid search)
Responsibilities
- Design and build scalable search ranking, indexing and personalization systems.
- 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.
- 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.
- 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.
- Real-time Personalization using query Embeddings for Search Ranking.
- Monitor model performance and continuously iterate using A/B testing and offline evaluation metrics.
- Experience in MLOps and model governance.
- Strong analytical and quantitative problem-solving ability.
- Deep expertise in distributed computing strategies in Azure, AWS or GCP Cluster, enhancing the parallel processing capabilities.
Preferred Qualifications
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No preferred qualifications provided.