Senior ML Data Engineer at Intuition Machines Inc

, , Croatia -

Full Time

Start Date

Immediate

Expiry Date

25 May, 26

Salary

0.0

Posted On

24 Feb, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Kafka, SQL, NoSQL, Clickhouse, AWS, Azure, Kubernetes, CI/CD, Orchestration, Microservice Design, Data Pipelines, Feature Engineering, Data Storage, ML Engineering, Software Development

Industry

Research Services

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. As a Senior ML Data 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. What will you do: 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. What we are looking for: 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. Nice to Have: Experience collaborating across ML, backend, and frontend teams. Understanding of machine learning fundamentals, including model training, inference, and frameworks such as PyTorch or TensorFlow. 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. We celebrate equality of opportunity and are committed to creating an inclusive environment for all team members. Join us as we transform cybersecurity, user privacy, and machine learning online! Please note that all positions require pre-employment screening, including third-party verification of work history, education, and identity, as well as a final in-person interview and identity verification step, which will be conducted in your country of residence.

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Responsibilities

The Senior ML Data Engineer will be responsible for shaping and expanding data pipelines powering enterprise security products and research efforts by designing, maintaining, and improving high-performance data workflows for high-velocity data. This includes providing interfaces for ML teams to build datasets on demand and collaborating across engineering teams to build out the data platform.