Software Engineer II, Search & Data Infrastructure -Slack

Salesforce•Seattle, WA

1d•$117,200 - $223,900

About The Position

Salesforce is seeking experienced engineers to join its Core Infrastructure organization within Slack, focusing on Search. This role involves designing, developing, and maintaining the information retrieval systems that power Slack Search and various product experiences. The search stack is crucial for enabling users to find information within workspaces and is foundational to generative AI efforts. The team works with technologies such as Java, Go, PHP/Hack, Python, MySQL, Solr, Kubernetes, Terraform, AWS, and Linux. The team is collaborative and works closely with infrastructure, machine learning, and product engineers. Slack fosters a positive, diverse, and supportive culture, valuing curiosity, inventiveness, and continuous improvement.

Requirements

4+ years of applicable software engineering experience.
Worked extensively with distributed data processing frameworks such as Spark, Hadoop, MapReduce, or EMR.
Hands-on experience with Solr, Elasticsearch, Lucene, or other search technologies.
Familiar with infrastructure/ops tools and frameworks such as Terraform, Chef, and Kubernetes.
Proficient with functional or imperative programming languages — e.g., PHP, Python, Ruby, Go, C, or Java.
A track record of building high-availability, performant, and scalable systems that handle large data volumes across multiple clusters and geographic regions.
Experience planning, implementing, and deploying software migrations and upgrades in production environments.
Can contribute meaningfully to technical architecture discussions and help drive sound technical decisions within your team.
A strong communicator — able to explain complex technical concepts clearly to designers, support staff, and fellow engineers.
Solid computer science fundamentals: data structures, algorithms, programming languages, distributed systems, and information retrieval.
A genuine, demonstrated AI-first approach to engineering — using AI tools to move faster, build fluency across the stack, and contribute beyond your core specialty.
Experience integrating AI tools (e.g., Claude Code, GitHub Copilot, Codex, Cursor) into your development workflow.
Advanced prompt engineering skills — able to write precise, structured prompts and cultivate the system context that makes AI outputs reliable, secure, and production-ready.

Nice To Haves

Built large-scale data pipelines professionally and can write clean, elegant code in Java, Scala, and/or SQL, with hands-on exposure to Hadoop/Spark.
Experience with text mining, NLP, or natural language parsing.
Contributed to relevance tuning, ranking systems, or query understanding.
Familiarity with vector search, dense retrieval, or embedding-based approaches to information retrieval.

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 growing!) user base.
Collaborate with team members to ensure high availability and reliability of a distributed search system at massive scale.
Own team features and systems end-to-end, defining their long-term health while proactively improving the health of surrounding systems.
Deploy, configure, test, troubleshoot, maintain, and upgrade Solr clusters and environments.
Continuously optimize the infrastructure and configuration of our Solr clusters to maintain cluster health and peak search performance.
Support our skilled operations team in triaging and resolving production issues quickly and effectively.
Raise the bar on engineering standards, tooling, and processes across the team.
Build and ship high-quality, production-grade software using modern engineering practices — with AI as a core part of your development workflow.
Design and orchestrate complex systems where AI agents integrate seamlessly into human workflows, driving efficiency and innovation at scale.
Contribute to building and maintaining 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.