Senior Software Developer (Search)

Design, implement, and operate core backend components of the search system, spanning request-time services and background data pipelines
Contribute to document ingestion, crawling, and preprocessing workflows, adapting strategies based on source, domain, and freshness requirements
Build and evolve indexing and retrieval systems, including data formats, update strategies, and access patterns
Implement and improve search request flows, including query processing, retrieval orchestration, and response assembly under strict latency budgets
Build well-tested services and pipelines with clear responsibilities and interaction contracts, while remaining flexible as the system evolves
Define and implement observability primitives, including structured logs, metrics, traces, and quality signals for both online and offline components
Support experimentation and iteration by enabling feature flags, controlled rollouts, and online experiments
Track throughput, latency, and resource usage across the system, and improve performance or cost efficiency when business needs require it
Collaborate closely with ML engineers to integrate semantic retrieval and ranking models, while keeping ML logic decoupled from core system internals
Work with data analysts and product managers to translate product and quality goals into concrete backend behavior and measurable metrics

Have 5+ years of experience as a software engineer working on production backend systems
Have hands-on experience with Go in real-world services (experience with other systems languages like C++ or Rust is a plus)
Have built concurrent, high-load systems, and are comfortable reasoning about throughput, latency, and failure modes
Are familiar with distributed systems fundamentals, including fault tolerance, load balancing, and horizontal scalability
Have operated your own code in production: deployed it, debugged incidents, rolled back changes when necessary, and understand what it means to interact directly with production systems — and when not to
Tend to think systematically by default, but can make pragmatic tradeoffs and cut corners intentionally when time or scope requires it
Are comfortable working across boundaries, reasoning about systems end-to-end rather than staying within a narrow component
Collaborate effectively in cross-functional teams, communicating clearly with engineers, ML practitioners, analysts, and product managers
Are curious about modern developer tooling and have used AI-assisted tools (coding agents, ChatGPT, etc.) in your workflow

Backend systems for search, recommendation, or other ranking-heavy products (e.g. search engines, ads platforms, content feeds, e-commerce)
Building or operating data pipelines, ingestion systems, or indexing workflows alongside online services
Practical exposure to ML-backed systems, including classical ML pipelines or LLM-based services
AI agent architectures, tool-calling systems, or agent-oriented workflows

Details

Design, implement, and operate core backend components of the search system, spanning request-time services and background data pipelines
Contribute to document ingestion, crawling, and preprocessing workflows, adapting strategies based on source, domain, and freshness requirements
Build and evolve indexing and retrieval systems, including data formats, update strategies, and access patterns
Implement and improve search request flows, including query processing, retrieval orchestration, and response assembly under strict latency budgets
Build well-tested services and pipelines with clear responsibilities and interaction contracts, while remaining flexible as the system evolves
Define and implement observability primitives, including structured logs, metrics, traces, and quality signals for both online and offline components
Support experimentation and iteration by enabling feature flags, controlled rollouts, and online experiments
Track throughput, latency, and resource usage across the system, and improve performance or cost efficiency when business needs require it
Collaborate closely with ML engineers to integrate semantic retrieval and ranking models, while keeping ML logic decoupled from core system internals
Work with data analysts and product managers to translate product and quality goals into concrete backend behavior and measurable metrics

Have 5+ years of experience as a software engineer working on production backend systems
Have hands-on experience with Go in real-world services (experience with other systems languages like C++ or Rust is a plus)
Have built concurrent, high-load systems, and are comfortable reasoning about throughput, latency, and failure modes
Are familiar with distributed systems fundamentals, including fault tolerance, load balancing, and horizontal scalability
Have operated your own code in production: deployed it, debugged incidents, rolled back changes when necessary, and understand what it means to interact directly with production systems — and when not to
Tend to think systematically by default, but can make pragmatic tradeoffs and cut corners intentionally when time or scope requires it
Are comfortable working across boundaries, reasoning about systems end-to-end rather than staying within a narrow component
Collaborate effectively in cross-functional teams, communicating clearly with engineers, ML practitioners, analysts, and product managers
Are curious about modern developer tooling and have used AI-assisted tools (coding agents, ChatGPT, etc.) in your workflow

Backend systems for search, recommendation, or other ranking-heavy products (e.g. search engines, ads platforms, content feeds, e-commerce)
Building or operating data pipelines, ingestion systems, or indexing workflows alongside online services
Practical exposure to ML-backed systems, including classical ML pipelines or LLM-based services
AI agent architectures, tool-calling systems, or agent-oriented workflows