Slack
AI CRM
StaffSoftwareEngineer,SearchDataInfrastructure
“Staff Software Engineer, Search Data Infrastructure at Slack. Skills: Search Data Infrastructure, Distributed data processing frameworks, Search technologies, Infrastructure and ops tooling, Programming languages, High-availability, performant, and scalable systems, Software migrations and upgrades, Technical architecture discussions, AI-first engineering, AI development tools integration, Prompt engineering. Design and develop scalable, resilient offline indexing pipelines that process and tran”
What You'll Achieve.
Drive significant business impact by contributing heavily to high-priority team projects; Deliver exceptional search experiences at scale; Deliver secure, optimized, and high-quality code at speed; Driving efficiency and innovation at scale
Industry & Context.
Troubleshoot; Triaging and resolving production issues
What They're Looking For.
Must Have
10+ years of hands-on experience with distributed data and search technologies, Worked extensively with distributed data processing frameworks such as Spark, Hadoop, MapReduce, or EMR, Experience with Solr, Elasticsearch, Lucene, or comparable search technologies, Familiar with infrastructure and ops tooling such as Terraform, Chef, and Kubernetes, Proficiency in functional or imperative programming languages — e. g. , PHP, Python, Ruby, Go, C, or Java, Track record of building high-availability, performant, and scalable systems that handle large volumes of data across multiple clusters in geographically distributed environments, Experience planning, implementing, and executing software migrations and upgrades in production environments, Confidence in contributing to technical architecture discussions and influencing technical decisions within your team, Bring a genuine, demonstrated AI-first approach to engineering — using AI to move faster, build fluency across the stack, and contribute well beyond your core specialty, Experience integrating AI development tools (e. g. , Claude Code, GitHub Copilot, Codex, Cursor) into day-to-day engineering workflows, Possess advanced prompt engineering skills — writing precise, structured prompts and cultivating system context that makes AI outputs reliable, secure, and production-ready
Nice to Have
Built large-scale data pipelines in a professional setting, Craft clean, elegant code in Java, Scala, and/or SQL, Solid exposure to Hadoop and/or Spark, Experience in text mining, parsing, and NLP techniques applied to search relevance or data enrichment, Contributed to or led efforts around search relevance tuning, ranking models, or query understanding
What You'll Do.
Design and develop scalable
resilient offline indexing pipelines that process and transform data for Slack's search infrastructure
Ensure high availability and reliability across our distributed search systems through thoughtful collaboration and rigorous engineering
Take long-term ownership of team features and systems
proactively improving their health and the health of surrounding services
and upgrade Solr clusters and environments
Continuously optimize Solr cluster infrastructure and configuration to sustain cluster health and search performance
Support our operations and customer-facing teams in triaging and resolving production issues efficiently
Build and ship production-grade software using modern engineering practices
with AI as a core pillar of your development workflow — leveraging AI tools to deliver secure
and high-quality code at speed
Design and orchestrate complex systems where AI agents integrate seamlessly into human workflows
driving efficiency and innovation at scale
Contribute to building and maintaining a shared system context — an explicit repository of system designs
and standards that enables AI to operate accurately and reliably
Critically evaluate code (human- or AI-generated) for correctness
How You'll Work.
Team & Collaboration
Partner with product engineering to conceptualize and ship new features; Thoughtful collaboration and rigorous engineering; Collaborative code reviews; Working closely with infrastructure engineers, ML engineers, product engineers, and product managers
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