Job description

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.

We are seeking a Machine Learning Engineer to build scalable multimodal (language and vision) ML models, deploy ML solutions to production, mentor fellow engineers, and contribute to our technical roadmap. The ideal candidate will translate business requirements into technical specifications and ensure our solutions meet performance, computational, and business constraints.

What you will do:

Implement, fine-tune, and evaluate the latest LLMs, large vision models, and multimodal approaches.
Clearly present the results of your experiments, including standard metrics and business goal evaluations.
Implement and deploy solutions iteratively: from POC to MVP to full-scale production.
Translate business requirements into technical specifications for language, vision, or multimodal solutions.
Provide technical mentorship to other ML engineers and researchers.
Iterate quickly, focusing on early and frequent deployment to ensure new products or features reach millions of users.
Write well-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 and engineering roadmap.

What we are looking for:

Thoughtful, conscientious, and self-directed individual.
5+ years of professional experience in ML.
Proven experience with the entire modeling lifecycle: building, evaluating, debugging, and productionalizing large ML models.
Strong experience with fine-tuning and deploying LLMs and large vision models.
Experience with generative vision and language models.
Strong understanding of ML fundamentals: bias-variance tradeoffs, loss functions, evaluation metrics, etc.
Bachelor's Degree or an 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 DevOps engineers to integrate your work into our infrastructure, including automating observability, deployment, quality, and security.