Applied Researcher 1

eBay•San Jose, CA

4d•$147,200 - $196,500•Hybrid

About The Position

eBay, Inc. seeks Applied Researcher 1 in San Jose, CA. This role involves investigating, prototyping, and evaluating state-of-the-art algorithms for time-series anomaly detection, forecasting, log pattern recognition, and graph analysis tailored for observability data. The position requires architecting and designing scalable distributed systems for processing massive volumes of observability data, and implementing analytical models to correlate signals across diverse data types for root cause analysis of production incidents. Additionally, the role involves developing and optimizing algorithms for analyzing distributed tracing data using graph theory, designing advanced alerting systems, and engineering software components for real-time data stream processing. Contributions to open-source observability projects or internal frameworks are also expected, along with creating technical documentation and presenting research findings.

Requirements

Master’s degree, or foreign equivalent, in Computer Science, Information Technology, or a closely related field plus two years of experience in the job offered or a related occupation.
Designing, building, and analyzing complex distributed systems (2 years)
Systems programming and scripting using Go, Java, and Python (2 years)
Observability and monitoring using Prometheus, Loki, Jaeger/Tempo, Grafana, and Kubernetes (2 years)
Large-scale data storage and querying systems (ClickHouse, Hadoop ecosystem) (2 years)
Practical experience in one or more of: time-series analysis, graph algorithms/analytics, statistical modeling and analysis techniques (including areas like anomaly detection, forecasting, pattern recognition/clustering) (2 years)
Must be legally authorized to work in the U.S. without sponsorship.

Responsibilities

Investigate, prototype, and evaluate state-of-the-art algorithms (from academia and industry) for time-series anomaly detection, forecasting, log pattern recognition, and graph analysis specifically tailored for observability data.
Architect and design highly scalable, resilient, and cost-efficient distributed systems for ingesting, processing, storing, and querying massive volumes (petabytes/day) of observability data.
Implement and operationalize analytical models and systems that correlate signals across diverse observability data (metrics, logs, traces, events) to facilitate automated or semi-automated root cause analysis of production incidents.
Develop and optimize algorithms and systems for analyzing distributed tracing data using graph theory and graph database technologies to understand service dependencies, critical paths, and performance impacts.
Design and refine systems for advanced alerting that minimize noise, automatically correlate related events, and deliver enriched context to on-call engineers, potentially using sophisticated data analysis techniques.
Engineer and optimize software components for real-time processing, aggregation, and enrichment of high-throughput observability data streams.
Potentially contribute improvements or research findings back to relevant open-source observability projects or develop internal frameworks/libraries to standardize observability practices.
Create detailed technical documentation for system designs, algorithms, and operational procedures.
Present research findings, system designs, and results to internal teams and potentially the broader technical community.