As an ML Applied Scientist, you will design, implement, and scale machine learning systems that power our products. You'll work across teams to translate business goals into technical specifications, ensuring our models perform efficiently under real-world constraints. This role combines research, engineering, and mentorship in a fast-paced production environment.

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.

What you will do:

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.

What we are looking for:

5+ years of professional experience in applied 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 in a technical field (or equivalent practical experience).
Thoughtful, self-directed individual who is comfortable making technical decisions independently.

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.

What we offer:

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.

Join us as we transform cybersecurity, user privacy, and machine learning online!

Intuition Machines, Inc.

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