Lead ML Engineer

Intuition Machines builds enterprise security products with an AI/ML focus. We apply our research to systems that serve hundreds of millions of people, with a team distributed around the world. You are probably familiar with our best-known product, the hCaptcha security suite. Our approach is simple: low overhead, small teams, and rapid iteration.

We’re looking for a Lead Machine Learning Engineer with strong software engineering skills and a creative mindset for ML-focused problem-solving. 

Using AI: Coding agents are indisputably useful tools. We provide access to the top 3 models, and were early adopters of evals-first development flows. Familiarity with coding using agents is part of all interviews. However, reliability and correctness are critical for us. You will need to read and understand every line of code with your name on it, and it will be reviewed by both people and machines.

• →Lead large-scale ML projects and products from inception to production, overseeing the entire lifecycle from design and implementation to deployment and maintenance.
• →Make key architectural decisions to ensure solutions are scalable, efficient, and maintainable while balancing business and technical constraints.
• →Drive collaboration across ML and engineering teams to ensure product success, influencing technical discussions and decisions at all levels.
• →Design and implement state-of-the-art machine learning pipelines and models that impact millions of users and generate real business value.
• →Set technical standards and lead the development of scalable, testable, and high-performance applications.
• →Provide leadership and mentorship to other ML engineers, fostering the growth of a strong Machine Learning Engineering organization.

• →Work on systems that affect millions of users daily, scaling machine learning systems with billions of data points and millions of inferences per second.
• →Gain experience in architecting and scaling advanced ML solutions for real-world challenges, moving beyond standard approaches from research papers.
• →Have the opportunity to define technical strategy, contribute to significant ML advancements, and guide the future direction of ML systems at scale.

• 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.