Lead ML Engineer

November 15

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Intuition Machines

WebsiteLinkedInAll Job Openings

machine learning and AI

51 - 200 employees

Founded 2014

🤖 Artificial Intelligence

🔒 Cybersecurity

☁️ SaaS

Description

• Intuition Machines builds enterprise security products with an AI/ML focus. • Seeking a Lead Machine Learning Engineer with strong software engineering skills and a creative mindset for ML-focused problem-solving. • Lead large-scale ML projects, making architectural decisions, and driving collaboration across teams. • Design and implement machine learning pipelines impacting millions of users. • Set standards and mentor other ML engineers, fostering growth within the organization. • Opportunity to scale systems with billions of data points and define technical strategy.

Requirements

• 7+ years of experience building and maintaining large-scale production ML systems, with a focus on performance, scalability, and reliability. • Proven experience as the owner or significant contributor to products used by tens of thousands of customers, with the ability to make complex design decisions. • Expertise in machine learning algorithms, productionizing ML models, and scaling systems to handle large data volumes. • Strong problem-solving skills with creative approaches to overcoming technical challenges. • Experience leading technical discussions, driving decisions, and setting technical standards for a team. • Excellent written and verbal communication skills, with the ability to articulate complex technical topics to both technical and non-technical stakeholders. • Familiarity with modern orchestration platforms (Kubernetes, containerization, microservice design) and distributed systems. • Ability to identify, define, and segment complex research problems and drive innovative solutions that align with business and technical goals.

Benefits

• Fully remote position with flexible working hours. • An inspiring team of colleagues spread all over the world. • Pleasant, modern development and deployment workflows: ship early, ship often. • High impact: lots of users, happy customers, high growth, and cutting edge R&D. • Flat organization, direct interaction with customer teams.