Senior Manager, Software Engineering - Employee Productivity
NVIDIA CorporationMatch Score
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Protocol Intelligence
Data-driven signals on your job's competitivenessStrong employer brand, metro location, and broad full-stack/AI leadership role create high candidate competition.
Role demands senior engineering leadership, applied AI, and enterprise integration experience, making industry/domain fit highly important.
Explicit 12+ years and 6+ years management plus required AI, cloud, security, and enterprise integration skills make filtering strict.
Job Description
Structured overview of role & requirementsAbout This Role
Lead and grow a high-performing engineering team building AI-powered employee productivity products across web, native mobile, and agentic AI interfaces.
Own engineering vision, architecture, multi-quarter roadmap, and operational health for a global employee productivity platform integrating HRIS, identity, collaboration, and knowledge systems.
Drive delivery of personalized search, content, workflow powered by LLMs and agentic AI, ensure product reliability, security, privacy, accessibility, and responsible AI are incorporated throughout.
Minimum Requirements
12+ years software engineering experience with at least 6 years managing engineering teams.
Degree required: BS, MS, or PhD in Computer Science, Electrical/Computer Engineering, Physics, Mathematics, or related field, or equivalent experience.
Demonstrated success delivering consumer-grade web or native mobile products at enterprise scale.
Strong experience with full-stack distributed systems, applied AI technologies (LLMs, agentic systems), cloud infrastructure, CI/CD, security, privacy, and compliance.
Ideal Candidate Profile
Experienced leader with proven ability to hire, mentor, and grow senior engineering talent in AI-enabled product teams.
Strategic thinker who can define and execute engineering architectures and roadmaps for large-scale, integrated employee productivity platforms.
Hands-on technical leader familiar with integrating HRIS and productivity ecosystems, building AI safety and governance frameworks, and scaling engineering organizations in fast-growing environments.