Staff/Sr. Software Engineer, Search Data Infrastructure - Slack

Salesforce•Seattle, WA

5d•$172,500 - $313,700

About The Position

Salesforce is seeking experienced engineers to join its Core Infrastructure organization at Slack. This team is responsible for designing, developing, and maintaining the information retrieval systems that power Slack Search and various product experiences. This infrastructure is crucial for enabling millions of users to instantly find information within their workspaces and is foundational to generative AI initiatives. The role involves pushing the boundaries of search technology, enhancing system reliability, performance, scalability, and security, and applying advances in information retrieval to a widely used platform. The team utilizes a stack including Java, Go, PHP/Hack, Python, MySQL, Solr, Kubernetes, Terraform, AWS, and Linux. Collaboration with infrastructure engineers, ML engineers, product engineers, and product managers is key. The culture values curiosity, craft, and collaboration, aiming for smart, humble, and hardworking individuals.

Requirements

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.
Familiarity 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.
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.
Advanced prompt engineering skills — writing precise, structured prompts and cultivating system context that makes AI outputs reliable, secure, and production-ready.

Nice To Haves

Built large-scale data pipelines in a professional setting and can craft clean, elegant code in Java, Scala, and/or SQL, with 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.

Responsibilities

Design and develop scalable, resilient offline indexing pipelines that process and transform data for Slack's search infrastructure.
Partner with product engineering to conceptualize and ship new features for our large — and rapidly growing — user base.
Ensure high availability and reliability across our distributed search systems through thoughtful collaboration and rigorous engineering.
Drive significant business impact by contributing heavily to high-priority team projects.
Take long-term ownership of team features and systems, proactively improving their health and the health of surrounding services.
Deploy, configure, test, troubleshoot, maintain, 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.
Conduct thorough, collaborative code reviews that raise the quality bar across the team.
Champion improvements to engineering standards, tooling, and development processes.
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, optimized, 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, constraints, and standards that enables AI to operate accurately and reliably.
Critically evaluate code (human- or AI-generated) for correctness, quality, security, and performance.