Senior ML Ops Engineer

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.