Machine Learning Engineer, AI Labs

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.