Research Engineer - Data Infrastructure

Seer is a research-driven AI company focused on building scalable intelligent systems capable of robust operation in dynamic environments. They are hiring Senior and Staff-level Data Infrastructure Machine Learning Engineers to scale the systems powering their ML training data platform, focusing on building and optimizing high-throughput data infrastructure and large-scale indexing and retrieval systems.

Responsibilities:

• Architect, build, and operate distributed data infrastructure capable of processing and managing billions of video and multimodal data samples
• Design systems with strong guarantees around reliability, latency, scalability, and cost efficiency
• Optimize cloud object storage, metadata systems, databases, and large-scale distributed storage architectures
• Build efficient indexing and retrieval systems to support rapid dataset querying, filtering, and iteration
• Improve data access patterns and retrieval performance for research and production ML workflows
• Design scalable metadata and search infrastructure for multimodal datasets
• Develop monitoring, alerting, failure recovery, and performance optimization frameworks for large-scale data pipelines
• Build tooling to identify bottlenecks and improve operational visibility across distributed systems
• Optimize workload balancing and throughput across distributed compute and storage infrastructure
• Build systems for artifact management, dataset versioning, lineage tracking, and reproducibility across training workflows
• Ensure traceability and consistency across evolving datasets and training runs
• Develop lightweight internal tooling enabling engineers and researchers to explore and analyze data at scale
• Integrate and scale vision-language model (VLM) inference within distributed data pipelines
• Support automated enrichment, filtering, metadata generation, and preprocessing workflows
• Collaborate closely with ML systems and research teams to improve data quality and training velocity

Requirements:

• 5+ years of experience in data infrastructure, distributed systems, ML infrastructure, or related fields
• Strong experience building and operating large-scale distributed data pipelines
• Deep understanding of: Distributed systems architecture, Databases and metadata systems, Indexing and retrieval strategies, Cloud storage architectures
• Experience optimizing throughput, workload balancing, and cost-performance tradeoffs in cloud environments
• Hands-on experience with distributed processing frameworks such as Ray or Spark
• Strong observability, monitoring, and production reliability experience
• Strong software engineering fundamentals with the ability to own systems end-to-end
• Experience managing large multimodal datasets
• Familiarity with ML training workflows and data lifecycle management
• Experience running large-scale ML inference workloads in distributed or cloud environments
• Familiarity with vision-language models (VLMs)
• Experience working with real-world sensor data such as video, telemetry, or time-series streams
• Familiarity with data warehouse technologies such as Snowflake, BigQuery, or Redshift
• Experience with data versioning and lineage systems such as DVC, Delta Lake, or similar tooling