Senior ML Ops Engineer
Intuition Machines Zobrazit všechny práce
- Česko
- Smlouva
- Plný úvazek
- 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.
- 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.