Staff Software Engineer, Search & Retrieval Infrastructure

Syllo

18d•$190,000 - $230,000•Remote

About The Position

Syllo is seeking a Staff Software Engineer to own and evolve its advanced search, indexing, and data-scanning infrastructure as the platform scales to multi-petabyte data volumes. The role focuses on balancing speed, availability, and cost: maintaining sub-second latency for interactive workflows while designing highly concurrent, cost-effective architectures for scanning and vectorizing large volumes of cold-storage data. The engineer will develop data-tiering and retrieval strategies that sustain platform performance while controlling cloud-compute costs.

Requirements

At least 8 years of software engineering experience.
Proven Staff- or Principal-level experience optimizing and scaling highly distributed, high-throughput systems to petabyte-level data volumes.
Deep production expertise tuning and scaling Lucene-based search engines such as Elasticsearch or Solr, and modern vector-indexing infrastructure.
Strong understanding of index internals, chunking strategies, and embedding-retrieval optimization.
Experience designing cost-effective architectures that balance compute and storage, including cold-storage scanning solutions and performant hot-index architectures.
Extensive experience managing complex data pipelines, high-throughput event streaming such as Kafka or Kinesis, and distributed compute architectures handling billions of records.
Expert command of cloud primitives, Kubernetes, and infrastructure-as-code.
Expert-level proficiency in systems-level and backend languages such as Go, Rust, Python, or Java/C++.

Nice To Haves

GCP experience is preferred.

Responsibilities

Lead optimization and architectural evolution of the hybrid search infrastructure, improving the throughput and efficiency of lexical search systems such as Elasticsearch and Lucene, as well as dense vector databases.
Design and implement cost-effective tiering strategies across hot, warm, and cold data states, and evolve distributed pipelines for asynchronous, large-scale scans of petabytes of data.
Reduce latency and cost-to-serve by analyzing bottlenecks, tuning indexing and querying algorithms, and optimizing cloud compute, storage, and networking.
Own the indexing and search ecosystem, set its long-term technical vision, and guide engineering teams on high-volume data modeling and performance tuning.
Ensure fault-tolerant, highly available operations during massive parallel ingestion and complex concurrent querying across millions of documents.