About ServiceNow
It all started when engineer Fred Luddy wrote code that automated a tedious task for his coworker, Phyllis. She cried tears of joy. Backed by Greylock and Sequoia.
About the role
For positions in this location, we offer a base pay of $202,500 - $354,400 , plus equity (when applicable), variable/incentive compensation and benefits. Sales positions generally offer a competitive On Target Earnings (OTE) incentive compensation structure. Please note that the base pay shown is a guideline, and individual total compensation will vary based on factors such as qualifications, skill level, competencies, and work location. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- 9+ years designing and building production data pipelines, data warehousing, ETL systems, strong expertise in SQL, Python, Javascript or equivalent, and distributed data processing
- 3+ years working on ML systems, model validation infrastructure, automated testing, or AI-powered applications, hands-on experience with data quality, feature engineering, and model monitoring
- Proven track record deploying, monitoring, and maintaining large-scale data systems in production, experience troubleshooting data quality issues, latency, and system reliability
- Ability to design scalable, secure data architectures, experience with cloud platforms (GCP, AWS), APIs, event-driven systems, and data governance patterns
- Familiarity with AI/ML evaluation frameworks, LLM-based systems, or agentic workflows, ability to instrument and evaluate complex AI systems, understanding of model performance tracking
- Comfortable driving technical conversations across multiple teams, takes end-to-end ownership, works autonomously, mentors peers on data engineering standards and best practices
More about this role
- Design & Architect Data Infrastructure — Design and oversee deployment of data architecture and pipelines to capture, manage, and store structured and unstructured data from internal and external sources; establish processes and data flows using cloud, local databases, and applicable storage forms
- Build & Automate Data Transformation — Develop technical tools using ML and data-engineering techniques to cleanse, organize, and transform data; implement automated processes to maintain data structure integrity and quality standards on an ongoing basis
- Define Agentic Evaluation Metrics & Ground Truth — Partner with product and AI teams to define evaluation metrics for agentic workflows (task completeness, tool use, workflow success); establish ground truth labeling standards and validation criteria; design evaluation datasets that reflect real-world agent execution
- Build Agentic Evaluation Pipelines — Design and implement automated evaluation infrastructure that measures AI agent performance using Now LLM Service models and execution logs; create dashboards, reporting, versioning, and reproducibility of evaluation datasets and results
- Establish Standards & Continuous...
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