Intuition Machines uses AI/ML to build enterprise security products. 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.

In Short

Build ML models that can scale to millions of requests per second while maintaining performance.
Translate business requirements into technical specifications.
Develop ML models that satisfy memory and compute constraints, evaluate them properly, and debug effectively.
Provide technical mentorship to other ML research engineers.
Iterate quickly, with a focus on shipping early and often, ensuring that new products or features can be deployed to millions of users.
Write clearly structured, maintainable, well-documented, and tested code, including unit, integration, and end-to-end tests.
Participate in code reviews and architecture & design sessions. Stay updated on recent technological developments and assess their applicability.
Provide technical input to the research roadmap.

Requirements

Thoughtful, conscientious, and self-directed.
5+ years of professional experience in ML.
Proven experience with the entire modeling lifecycle: building, evaluating, and debugging large ML models.
Experience with large-scale categorical and structured data.
Expertise in real-time ML models, incremental learning, and online learning.
Strong understanding of ML fundamentals: bias-variance tradeoffs, loss functions, evaluation metrics, etc.
Bachelor's Degree or foreign equivalent in a technical or related field from an accredited college or university, or equivalent job experience.
Excellent communication, listening, and presentation skills to engage with diverse audiences and support and mentor peers.

Nice to Have

Strong grasp of the math required for ML (linear algebra, probability theory, statistics, matrix calculus).
Software engineering/development experience with large-scale distributed systems.
Ability to collaborate with ML engineers to integrate your work into our infrastructure, including automating observability, deployment, quality, and security.