Distinguished Engineer, Machine Learning

Netskope•Santa Clara, CA

About The Position

Within Netskope Engineering, the Netskope AI Labs is responsible for advancing state-of-the-art artificial intelligence (AI) and machine learning (ML) technology to power the Netskope One security platform. We are seeking a visionary Distinguished Machine Learning Engineer to serve as the chief architect of our AI technical strategy, bridging the gap between cutting-edge AI research and enterprise-scale business impact. Leveraging our deep expertise in AI/ML and security, you will lead the architecture and deployment of large-scale, production-grade AI/ML solutions for our Secure Access Service Edge (SASE) architecture. This is a highly impactful leadership opportunity to shape the AI transformation of a market-leading cloud security company. A successful candidate will serve as a primary technical pillar for the organization, possessing deep technical expertise in applying AI/ML technologies to complex security applications. You will have the platform to build a scalable 'AI Engine', working alongside top-tier engineers, researchers, and machine learning scientists to solve the industry's most challenging AI latency, scalability, and cloud security problems today.

Requirements

12-15+ years of industry experience (or equivalent combination of an Advanced technical degree + experience) in architecting and developing AI/ML solutions, preferably on large-scale security products or services.
Fluency in the modern AI stack with proven, hands-on experience optimizing cutting-edge LLMs in production environments, with deep knowledge of vLLM, SGLang, and KV Cache optimization.
Ability to translate complex technical architectures and concepts between CXOs, non-technical stakeholders, and Data Scientists.
Energetic self-starter with a true startup spirit, demonstrated ability to influence without authority, and the willingness to wear multiple hats to deliver end-to-end solutions in a dynamic, fast-paced environment.

Responsibilities

Define the AI Transformation Roadmap: Drive the overarching AI/ML technical strategy, ruthlessly prioritizing architectural choices to ensure highly scalable, reliable, and production-grade systems.
Architect Production-Grade Inference Systems: Design, optimize, and deploy highly scalable AI/ML inference systems, leveraging the latest LLM serving technologies such as vLLM, SGLang, and advanced KV Cache optimization to maximize throughput and minimize latency.
Lead the End-to-End AI Lifecycle: Collaborate with ML scientists, engineers, and executive stakeholders to translate complex business requirements into industrialized enterprise solutions.
Establish Rigorous AI Evaluation: Create strict 'Report Cards' for AI models in production, ensuring models are robust, scalable, and well-documented by measuring accuracy, latency, and security relevance before deployment.