Senior ML Ops Engineer

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.

As a Senior ML Ops Engineer, you will help shape and expand the pipelines that power our products and research efforts. You’ll work across teams to design, maintain, and improve high-performance data pipelines, ensuring that data is accessible, reliable, and scalable to meet the needs of our users and internal stakeholders.

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.

• Maintain, extend, and improve existing data/ML workflows, and implement new ones to handle high-velocity data.
• Provide interfaces and systems that enable ML engineers and researchers to build datasets on demand.
• Influence data storage and processing strategies.
• Collaborate with the ML team, as well as frontend and backend teams, to build out our data platform.
• Reduce time-to-deployment for dashboards and ML models.
• Establish best practices and develop pipelines and software that enable ML engineers and researchers to efficiently build and use datasets.
• Work with large datasets under performance constraints comparable to those at the largest companies.
• Iterate quickly, with a focus on shipping early and often, ensuring that new products or features can be deployed to millions of users.

• Minimum of 3 years of experience in a data role involving designing and building data stores, feature engineering, and building reliable data pipelines that handle high loads.
• At least 2 years of professional software development experience in a role other than data engineering.
• Proficiency in Python and experience working with Kafka infrastructure and distributed data systems.
• Deep understanding of SQL and NoSQL databases (preferably Clickhouse).
• Familiarity with public cloud providers (AWS or Azure).
• Experience with CI/CD and orchestration platforms: Kubernetes, containerization, and microservice design.
• Proven ability to make independent decisions regarding data processing strategy and architecture.
• Thoughtful, self-directed individual who is able to operate effectively in a fast-paced environment.

• Experience collaborating across ML, backend, and frontend teams.
• Understanding of machine learning fundamentals, including model training, inference, and frameworks such as PyTorch or TensorFlow.