> Markdown version of [/jobs/ext/1470722-machine-learning-engineer-ai-labs](https://www.wearedevelopers.com/jobs/ext/1470722-machine-learning-engineer-ai-labs). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer, AI Labs - **Company:** Netskope - **Location:** Santa Clara, United States - **Experience:** Experienced - **Salary:** $128,000.0 - $260,500.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Cloud Computing Security, Distributed Systems, Memory Management, Machine Learning, Microsoft Cluster Server, Software Engineering, Large Language Models, Low Latency, TensorRT - **Published:** July 28, 2026 - **Apply:** https://www.dice.com/job-detail/3387da44-4fd4-4b3e-84ec-53df0949f6e4 ## About the Role * 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.Education * BSCS or equivalent required, MSCS or equivalent strongly preferred. ## Description 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. What's in it for You? * 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. What You Will Do * 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. ## Related Videos - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated) - [Swapping Low Latency Data Storage Under High Load](https://www.wearedevelopers.com/videos/746-swapping-low-latency-data-storage-under-high-load) - [Efficient deployment and inference of GPU-accelerated LLMs​](https://www.wearedevelopers.com/videos/929-efficient-deployment-and-inference-of-gpu-accelerated-llms) - [How Machine Learning is turning the Automotive Industry upside down](https://www.wearedevelopers.com/videos/61-how-machine-learning-is-turning-the-automotive-industry-upside-down) - [What non-automotive Machine Learning projects can learn from automotive Machine Learning projects](https://www.wearedevelopers.com/videos/397-what-non-automotive-machine-learning-projects-can-learn-from-automotive-machine-learning-projects) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Stephan Gillich - Bringing AI Everywhere](https://www.wearedevelopers.com/magazine/489-stephan-gillich-bringing-ai-everywhere) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)