Staff ML Engineer, Generative AI Platform—Equity
ServiceNow
An application made for this job — a tailored resume and cover letter that speak straight to the posting.
Benefits offered by this job
Job summary
ServiceNow is seeking a highly skilled engineer to scale and optimize a cutting-edge Generative AI product for enterprise users. You will build scalable infrastructure, design API abstractions, and collaborate across ML, application, product, and support teams to drive feature-rich improvements across multiple chat channels.
Join a team delivering high‑impact AI capabilities, with emphasis on performance, multilingual support, and robust engineering practices in a large-scale enterprise
Qualifications
- 7+ years of professional development experience building scalable systems.
- Bachelor's degree or higher in computer science or related field.
- Strong knowledge of API design and coding practices.
- Experience with tracing, logging, and metrics frameworks.
- Excellent cross-functional collaboration and communication.
- Ability to research requirements and deliver rapid iterations.
Responsibilities
- Build extensive product infrastructure and configurable interfaces for generative AI models.
- Design scalable API abstractions for multi-client chat integrations.
- Optimize the dialog engine for latency, memory footprint, and multilingual translation.
- Champion best coding practices and drive engineering excellence.
- Provide insights with robust logging, tracing, and metrics tools.
- Collaborate with ML, application, product, and support teams on new features.
Skills
Education
Job description
ServiceNow is seeking a highly skilled engineer to scale and optimize a cutting-edge Generative AI product for enterprise users. You will build scalable infrastructure, design API abstractions, and collaborate across ML, application, product, and support teams to drive feature-rich improvements across multiple chat channels.
Join a team delivering high‑impact AI capabilities, with emphasis on performance, multilingual support, and robust engineering practices in a large-scale enterprise
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