SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Salary: USD 176,100 - 308,200 / annual
ServiceNow's Advanced Technology Group (ATG) is seeking a Staff Machine Learning Engineer to design and build AI and machine learning solutions that power intelligent enterprise services. You will work on models, algorithms, and pipelines that make enterprise software easier to use for any user, regardless of technical background, serving 5,000+ enterprise customers worldwide.
In this role, you will develop and deploy cloud-based ML and AI solutions, design and train models including LLM-based approaches, turn research ideas into production-ready systems, and de-risk emerging AI techniques before they reach customers. You'll partner with product owners to translate requirements into measurable ML outcomes and collaborate daily with applied scientists, engineers, and product managers to deliver quality software from design through monitoring.
The Advanced Technology Group has a dual mission: building and evolving ServiceNow's AI platform while partnering with teams to create AI-powered work experiences. You'll be at the intersection of research and production, responsible for taking experimental techniques and making them reliable, scalable systems that serve enterprise customers.
REQUIREMENTS:
- Experience leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving
- Hands-on experience applying core ML/AI techniques and algorithms (supervised and unsupervised learning, deep learning, NLP, or LLMs)
- Strong programming skills in Python (Java acceptable) with solid software engineering fundamentals
- Experience evaluating model performance, including metrics, error analysis, and experiment design
- Familiarity with unit testing, profiling, and performance tuning for ML systems
DESIRED QUALIFICATIONS:
- Experience building new products on high-performance or large-scale ML systems
- Experience with LLM fine-tuning, retrieval-augmented generation, or agentic AI frameworks
- Publications, research contributions, or open-source ML work
- Experience with MLOps: model deployment, monitoring, and retraining pipelines
