Senior ML Ops Engineer — Remote, High-Impact Data Pipelines

Intuition Machines, Inc.; United States; A distancia; USD 140.000 - 190.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Fully remote

Flexible working hours

Global team

Modern deployment workflows

Descripción de la vacante

Intuition Machines, Inc. is seeking a Senior ML Ops Engineer to design, maintain, and scale high-performance data pipelines powering our AI-driven security products.

You will collaborate with ML researchers, frontend and backend teams to enable on-demand dataset creation, efficient deployment, and robust data platforms. A fast-paced, globally distributed environment awaits with a focus on reliability and scale.

Formación

• 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.

Responsabilidades

• Maintain, extend, and improve existing data/ML workflows to handle high-velocity data.
• Provide interfaces for ML engineers to build datasets on demand.
• Influence data storage and processing strategies.
• Collaborate with ML, frontend, and backend teams to build out the data platform.
• Reduce time-to-deployment for dashboards and ML models.
• Establish pipelines and software for efficient dataset use by ML researchers.
• Work with large datasets under performance constraints similar to large companies.
• Iterate quickly to ship new products/features to millions of users.

Conocimientos

Python

Data pipelines

Feature engineering

SQL

Kafka

Distributed data systems

Data modeling

CI/CD

Debugging

Herramientas

Kubernetes

Docker

ClickHouse

Cloud platforms (AWS/Azure)

Descripción del empleo

Intuition Machines, Inc. is seeking a Senior ML Ops Engineer to design, maintain, and scale high-performance data pipelines powering our AI-driven security products.

You will collaborate with ML researchers, frontend and backend teams to enable on-demand dataset creation, efficient deployment, and robust data platforms. A fast-paced, globally distributed environment awaits with a focus on reliability and scale.