Minimum of 3 years of experience in a data role involving designing and building data stores, feature engineering, and reliable, high-load data pipelines.
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 such as AWS or Azure.
Experience with continuous integration and continuous delivery, Kubernetes, containerization, and microservice design.
Ability to make independent decisions regarding data processing strategy and architecture.
Ability to operate effectively in a fast-paced environment.
Read and understand every line of code bearing the candidate’s name; familiarity with coding using agents is part of all interviews.
Complete pre-employment screening, including third-party verification of work history, education, and identity, and a final in-person interview and identity verification in the candidate’s country of residence.
Responsibilities
Maintain, extend, and improve existing data and machine learning workflows, and implement new workflows to handle high-velocity data.
Provide interfaces and systems that enable machine learning engineers and researchers to build datasets on demand.
Influence data storage and processing strategies.
Collaborate with the machine learning, frontend, and backend teams to build out the data platform.
Reduce time to deployment for dashboards and machine learning models.
Establish best practices and develop pipelines and software that enable machine learning 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, shipping early and often, so new products or features can be deployed to millions of users.
Desired Qualifications
Experience collaborating across machine learning, backend, and frontend teams.
Understanding of machine learning fundamentals, including model training, inference, and frameworks such as PyTorch or TensorFlow.
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.
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.