תיאור המשרה המלא
המשרה המקורית · נשמר לעיוןLasso 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 looking for an AI Team Lead to lead our ML engineering team — the innovation engine behind Lasso's platform. This is both a people-leadership role and a hands-on engineering role. You will own your team's delivery: running sprints, holding the team to its estimations, and removing blockers, while staying deeply technical yourself — designing architectures, reviewing work, and writing production code alongside the team.
Your team builds the machine learning that protects AI-powered applications, agentic workflows, and intelligent systems, spanning classical ML through advanced LLM pipelines, deployed at enterprise scale. The ideal candidate has already led a team of ML engineers, knows how to ship research-grade ideas into robust production systems, and is driven to keep Lasso at the forefront of AI security.
Responsibilities
• Team Leadership & Delivery
• Lead, mentor, and grow a team of ML engineers, owning their professional development and day-to-day technical direction
• Own the team's sprint cycle end-to-end — planning, estimation, and execution — and ensure work is delivered on scope and on schedule
• Remove blockers, balance priorities, and keep the team focused on the highest-impact work
• Partner with Product, Engineering, and Security leadership to align the team's roadmap with company goals
Hands-On Engineering
• Lead from the front: design architectures, review technical work, build prototypes, and write production-quality code alongside the team
• Design, train, and deploy machine learning models at enterprise scale, spanning classical ML algorithms to advanced LLM pipelines
• Engineer robust data ingestion and inference pipelines capable of handling high-throughput production workloads
• Apply MLOps and LLMOps best practices — model versioning, experiment tracking, automated testing, and deployment pipelines — across the team's work
Research & Innovation
• Keep the team at the cutting edge of AI security research, identifying and adapting emerging ML techniques and algorithms for practical cybersecurity applications
• Research and implement state-of-the-art ML solutions to protect AI-powered applications, agentic workflows, and intelligent systems from sophisticated threats
• Drive rapid iteration cycles from initial prototype through full production deployment, continuously raising the bar on what the team ships
Cross-Functional Collaboration
• Partner closely with AI researchers, engineering managers, and product teams to transform innovative concepts into reliable, scalable ML systems
• Translate complex analytical insights into actionable, production-ready solutions
• Represent the team's technical decisions and trade-offs to stakeholders across the organization
Requirements
• 5+ years of combined experience in machine learning engineering and data science, with demonstrated expertise in deploying ML solutions in production environments
• 2+ years of experience directly managing a team of ML engineers, including ownership of sprint planning, estimation, and delivery
• Advanced proficiency in Python programming, with extensive experience using core ML libraries including scikit-learn, PyTorch, and Transformers
• Strong background in API development using FastAPI, microservices architecture, and building scalable backend systems
• Experience with MLOps practices including model versioning, experiment tracking, automated testing, and deployment pipelines
• Experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies including Docker
• Proficiency with data processing technologies including pandas or Polars, SQL and NoSQL databases for large-scale data handling
• Proven ability to translate research concepts into production-grade systems with appropriate monitoring, testing, and reliability measures
• A hands-on leadership style: still actively writing and reviewing code, with the technical depth to guide architecture and unblock the team
• Strong analytical and problem-solving skills with attention to detail and system reliability
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
• Track record of contributing to open source projects, publishing research, or technical writing
• Experience building and scaling a team from its early stages
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