Senior Software Engineer (MLOps) - Feature Store
- Design and build distributed feature pipelines that power both training and low-latency inference use cases.
- Implement feature versioning, cataloging, and access management capabilities, ensuring traceability and governance across teams.
- Drive the integration of embedding storage systems (e.g., DDVector) to support scalable training and real-time retrieval.
- Build tooling to surface feature metadata, dependencies, and usage analytics to empower discovery and reuse.
- Partner with training, serving, and registry teams to deliver seamless integration across the AI Platform lifecycle.
- Own the operational excellence of your systems through monitoring, performance optimization, and cost efficiency.
- You have 6+ years of experience as a data or platform engineer, including designing large-scale distributed systems for ML or analytics workloads.
- You've built and operated data pipelines or storage systems with technologies like Kafka, Snowflake, ClickHouse, dbt, or similar.
- You are proficient in Python, Scala, Go, or Java, and have production experience working in cloud environments (e.g., AWS).
- You understand data model design and storage formats optimized for ML workflows (e.g., columnar formats, LSM trees).
- You value cross-functional collaboration and can work closely with applied scientists, backend engineers, and infra teams.
- Bonus: experience building or operating a feature store, embedding database, or metadata management platform.
- New hire stock equity (RSUs) and employee stock purchase plan (ESPP)
- Continuous professional development, product training, and career pathing
- Intradepartmental mentor and buddy program for in-house networking
- An inclusive company culture, ability to join our Community Guilds (Datadog employee resource groups)
- Access to Inclusion Talks, our internal panel discussions
- Free, global mental health benefits for employees and dependents age 6+
- Competitive global benefits
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