Staff Data Infrastructure Engineer for Large-Scale ML

Rhoda AI; Mountain View (WY); On-site; USD 180,000 - 260,000; Full time

Job summary

Rhoda AI is seeking Data Infrastructure MLEs to scale our data pipelines and model-training data infrastructure for billions of video clips. You will design high-throughput storage, indexing, and retrieval systems for multimodal datasets, while ensuring reliability, observability, and cost efficiency.

You will own end-to-end data systems from ingestion to production, implement workload balancing across distributed compute and storage, and collaborate with researchers to enable scalable data

Qualifications

• 5+ years of experience in data infrastructure, distributed systems, ML infrastructure, or related field.
• Experience building and operating large-scale data pipelines (1B+ samples or petabyte-scale systems).
• Deep understanding of distributed systems, databases, indexing strategies, and cloud storage architectures.
• Experience with throughput optimization and cost-performance tradeoffs in cloud environments.
• Familiarity with Ray or Spark for large-scale data processing.
• Strong observability, monitoring, and production reliability capabilities.
• Ability to own systems end-to-end from design to production.

Responsibilities

• Architect, build, and scale a high-throughput data infrastructure that processes and manages billions of video clips with strong guarantees around reliability, latency, and cost efficiency
• Design and optimize large-scale storage systems (cloud object storage, databases, metadata stores) for multimodal datasets
• Build efficient indexing and retrieval systems to support fast dataset querying, filtering, and iteration for research and production use cases
• Develop observability frameworks for data pipelines including monitoring, alerting, failure recovery, and performance optimization
• Implement intelligent workload balancing and throughput optimization across distributed compute and storage systems
• Manage data artifacts, versioning, and lineage to ensure reproducibility and traceability across training runs
• Build internal interfaces and lightweight tools that enable researchers and engineers to explore, query, and analyze large datasets at scale
• Support integration and scalable deployment of vision-language models (VLMs) within data pipelines for screening, enrichment, or metadata generation

Skills

Data infrastructure

Distributed systems

ML infrastructure

Large-scale data pipelines

Cloud storage architectures

Observability & reliability

End-to-end ownership

Tools

Ray

Spark

Snowflake

BigQuery

Kubernetes

Job description

Rhoda AI is seeking Data Infrastructure MLEs to scale our data pipelines and model-training data infrastructure for billions of video clips. You will design high-throughput storage, indexing, and retrieval systems for multimodal datasets, while ensuring reliability, observability, and cost efficiency.

You will own end-to-end data systems from ingestion to production, implement workload balancing across distributed compute and storage, and collaborate with researchers to enable scalable data