Machine Learning Engineer

Netskope•Santa Clara, CA

46d•$128,000 - $260,500•Remote

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 Intelligent Security Service Edge (SSE) security platform. We are seeking a dedicated Machine Learning Engineer to help develop and deploy enterprise-scale AI solutions. Leveraging your growing expertise in AI/ML, you will work closely with senior architects to build and maintain production-grade AI/ML solutions for our Secure Access Service Edge (SASE) architecture. This is a highly impactful opportunity to contribute to the AI transformation of a market-leading cloud security company. You will have the platform to help 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

2+ years of industry experience (or equivalent combination of a technical degree + experience) in developing AI/ML solutions.
Hands-on experience with the modern AI stack, including exposure to optimizing LLMs in production environments and familiarity with tools like vLLM, SGLang, and KV Cache optimization.
Strong communication skills with the ability to discuss technical concepts clearly with cross-functional team members.
Energetic self-starter with a true startup spirit and the willingness to wear multiple hats to deliver end-to-end solutions in a dynamic, fast-paced environment.

Responsibilities

Contribute to the AI Roadmap: Assist in executing the AI/ML technical strategy by building highly scalable, reliable, and production-grade systems.
Develop Production-Grade Inference Systems: Help design, optimize, and deploy scalable AI/ML inference systems, working with modern LLM serving technologies such as vLLM, SGLang, and advanced KV Cache optimization to maximize throughput and minimize latency.
Participate in the End-to-End AI Lifecycle: Collaborate with ML scientists, senior engineers, and product stakeholders to translate business requirements into enterprise solutions.
Implement AI Evaluation: Help enforce strict 'Report Cards' for AI models in production by measuring and tracking accuracy, latency, and security relevance before and during deployment.