Senior ML Engineer

Description Lasso is the AI Security Platform built for the agentic era. As organizations race to deploy AI agents and agentic applications, Lasso provides the only continuous security loop spanning discovery, AI security posture management, automated red teaming, and runtime protection. By connecting these capabilities in a single platform, Lasso gives security teams complete visibility over their AI environment, proactive risk management, next-generation adversarial testing, and real-time threat response ensuring every agent behaves within its intended scope at every stage of its lifecycle. We are seeking an exceptional Senior Machine Learning Engineer who excels at bridging cutting-edge AI and machine learning research with practical, scalable product deployment. This role is perfect for a professional who thrives on taking full ownership of projects from conception to completion, tackling complex challenges from 0 to n at the intersection of AI and cybersecurity. The ideal candidate has a passion for not only conducting research but also transforming findings into robust, production-ready systems while driving end-to-end project delivery. Responsibilities: Research & Innovation Research and implement state-of-the-art machine learning solutions to protect AI-powered applications, agentic workflows, and intelligent systems from sophisticated threats Stay at the forefront of AI security research, identifying and adapting emerging ML techniques and algorithms for practical cybersecurity applications Model Development & Deployment Design, train, and deploy machine learning models at enterprise scale, spanning classical ML algorithms to advanced LLM pipelines Optimize model performance across multiple dimensions including accuracy, latency, and computational cost Apply MLOps and LLMOps best practices and methodologies to enhance security-focused AI models Infrastructure & Engineering Engineer robust data ingestion and inference pipelines capable of handling high-throughput production workloads Develop and maintain scalable services using Python and FastAPI frameworks Implement streaming data architectures and CI/CD processes that ensure models remain current and reliable in dynamic threat environments Cross-Functional Collaboration Partner closely with AI researchers, engineering managers, and product teams to transform innovative concepts into reliable, scalable ML systems Collaborate with Product, Engineering, and Security teams to translate complex analytical insights into actionable, production-ready solutions Drive rapid iteration cycles from initial prototype through full production deployment Requirements: 5+ years of combined experience in machine learning engineering and data science, with demonstrated expertise in deploying ML solutions in production environments Advanced proficiency in Python programming, with extensive experience using core ML libraries including scikit-learn, PyTorch, and Transformers Proficiency with data processing technologies including pandas or Polars, SQL and NoSQL databases for large-scale data handling Experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies including Docker Strong background in API development using FastAPI, microservices architecture, and building scalable backend systems Proven ability to translate research concepts into production-grade systems with appropriate monitoring, testing, and reliability measures Experience with MLOps practices including model versioning, experiment tracking, automated testing, and deployment pipelines Strong analytical and problem-solving skills with attention to detail and system reliability Track record of contributing to open source projects, publishing research, or technical writing Advantages: Experience in cybersecurity or security analytics Background working with Large Language Models, including hands-on experience with training, fine-tuning, and deployment Hands-on experience with LLM frameworks and tools such as LangChain, Hugging Face, OpenAI APIs, and vector databases