Machine Learning Engineer, AI Labs

Today, there's more data and users outside the enterprise than inside, causing the network perimeter as we know it to dissolve. We realized a new perimeter was needed, one that is built in the cloud and follows and protects data wherever it goes, so we started Netskope to redefine Cloud, Network and Data Security. 

Within Netskope Engineering, Netskope AI Labs is the powerhouse advancing state-of-the-art artificial intelligence (AI) and machine learning (ML) to protect the modern enterprise. We build the intelligence behind the Netskope Intelligent Security Service Edge (SSE) platform.

We are seeking a high-caliber Machine Learning Engineer to help us build, optimize, and deploy enterprise-scale AI solutions. Working closely with senior architects, you will directly influence our Secure Access Service Edge (SASE) architecture, turning cutting-edge AI research into production-grade reality.

Note on Leveling: We believe great talent doesn't always fit into a rigid box. Candidates are assessed individually and leveled (from mid to senior) according to their specific skills, background, and technical depth.

• High-Impact Ownership: You aren't just maintaining pipelines; you are playing a critical role in the AI transformation of a market-leading cloud security company.
• Cutting-Edge Stack: Work on the bleeding edge of LLM inference optimization, utilizing tools like vLLM, SGLang, and advanced KV Cache optimization.
• Elite Collaboration: Work alongside top-tier engineers, researchers, and ML scientists to solve the industry’s toughest challenges in latency, throughput, and cloud security.

• →Collaborate on the AI Roadmap: Play a key role alongside senior architects and team members in driving the execution of critical AI/ML technical strategies, building highly scalable, reliable, and production-grade systems.
• →Architect High-Performance Inference Systems: Design, optimize, and deploy enterprise-scale LLM serving infrastructures. You will push the boundaries of throughput and latency.
• →Own the End-to-End AI Lifecycle: Partner closely with ML scientists and product stakeholders to translate complex business requirements into elegant, deployed code.
• →Enforce AI Excellence: Implement and scale strict "Report Cards" for production models, tracking real-world accuracy, latency, and security relevance.

• Industry Experience: 10+ years of overall experience in software engineering and product development, with a specialized focus in one of two tracks:
• The AI/ML Focus: 2+ years of production experience developing, optimizing, and deploying AI/ML solutions (or an equivalent blend of an advanced technical degree + hands-on experience).
• The Distributed Systems Focus: 6+ years of deep experience architecting, building, and scaling high-performance distributed systems, combined with a strong desire to apply those infrastructure skills to cutting-edge AI/LLM engineering.
• The Modern AI Stack: Direct exposure to (or a strong conceptual understanding of) optimizing LLMs in production. Familiarity with high-throughput inference frameworks (e.g., vLLM, SGLang, TensorRT-LLM) and memory management techniques like KV Cache optimization is a massive plus.
• Clear Communication: The ability to distill complex technical architecture or infrastructure bottlenecks into clear, actionable concepts for cross-functional teams.
• The Startup Mindset: You are an energetic self-starter who thrives in fast-paced, dynamic environments and isn't afraid to wear multiple hats to get a product across the finish line.

• BSCS or equivalent required, MSCS or equivalent strongly preferred.