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Salesforce
Software Engineering PMTS - Search & Personalization
Career Insights for Software Developer / Engineer
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Based on California data
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What they do
A Software Developer or Engineer designs or improves computer software. Oversees the entire software development process. Analyzes customer or user needs, designs programs, writes code or instructs computer programmers, tests design, and documents programs. May assist with upgrades or maintenance. May specialize in the design of computer applications or computer systems.
$162,138 / year median in California
-13% projected decline
Job Description
To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts. Job Category Software Engineering Job Details About Salesforce Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn't a buzzword
Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Salesforce will consider for employment qualified applicants with arrest and conviction records. At Salesforce, we believe in equitable compensation practices that reflect the dynamic nature of labor markets across various regions. The typical base salary range for this position is $197,300
- it's a way of life.
- the AI-powered discovery layer that sits at the heart of how customers find and engage with solutions across the Salesforce ecosystem. As a Principal Engineer on the Search & Personalization team, you will be the technical authority for the systems that determine what customers see, find, and click on across AgentExchange. You'll design and deliver the next generation of agentic search
- multi-turn, context-aware, and enriched with AI overviews, next-best query suggestions, and personalized recommendations
- alongside the indexing infrastructure and contextual discovery services that power embedded surfaces across Agentforce, Slack, Setup, and beyond.
- Agentforce, MuleSoft, Tableau, and Slack
- while delivering full transactability (Discover → Purchase → Provision).
What You'll Do:
Lead architectural design and delivery of major Search & Personalization capabilities, including: GenAI search- AI overview results, next-best query suggestions, and natural language query understanding Multi-turn agentic search
- conversational discovery with recommendations, comparisons, and contextual follow-up Personalization service
- behavioral and preference-based ranking and recommendations Contextual discovery service
- powering embedded AgentExchange discovery surfaces within Agentforce, Slack, Setup, and other core product surfaces Search indexer
- scalable, low-latency indexing pipelines for new asset types including standards-based agents, MCP tools/resources, skills, subagents, and A2A listings Champion AI-native engineering
- actively use and promote AI tools across the development lifecycle, from code completion to production monitoring. Model AI fluency and help build an AI-native team culture. Define the Northstar architecture for the Search & Personalization domain
- leading discovery, design, and execution tracks that align with the broader AgentExchange one-marketplace strategy. Drive platform modernization
- contribute to the migration off Heroku and onto Falcon, including the enabling roadmap for search services infrastructure migration. Own engineering excellence across the search stack: observability and telemetry (SLOs/SLIs, latency budgets, relevance metrics), safe change practices, 99.95% availability targets, and security risk governance. Act as the technical authority for your domain
- providing guidance, mentorship, and sponsorship to senior and junior engineers, fostering a culture of technical excellence and bold innovation.
- embody our values of Trust, Innovation, Engineering Commitment, and Curiosity.
Requirements:
10+ years of experience in software engineering, with a demonstrated track record of technical leadership on enterprise-scale search, recommendation, or personalization systems. Proven ability to define and drive long-term technical vision and architectural roadmaps for large, complex search or ML-infused platform domains. Deep expertise in building and operating search systems at scale: Search engine internals: indexing pipelines, ranking, query understanding, relevance tuning Search infrastructure: Elasticsearch, OpenSearch, Solr, or equivalentBack End:
Node.js, Java, or equivalent for high-throughput search services Experience designing and delivering recommendation or personalization systems- collaborative filtering, content-based ranking, behavioral signal processing, or equivalent. Hands-on experience integrating LLMs or GenAI into search products
- AI-generated summaries, query expansion, semantic search, or conversational retrieval.
Strong commitment to engineering excellence:
observability, telemetry, latency SLOs, CI/CD, service ownership, safe change practices, and production reliability. Excellent communication skills- able to influence and build consensus across engineering, product, and executive stakeholders.
Preferred Qualifications:
Experience building multi-turn or conversational search experiences, including context management and session-aware ranking. Hands-on experience with agentic systems, MCP tools/resources, or A2A patterns- particularly catalog and metadata requirements for new AI-native asset types. Experience with contextual or embedded discovery surfaces
- delivering search within host product surfaces (e.g. in-app, in-builder, in-Slack contexts). Familiarity with marketplace or e-commerce search
- faceted navigation, intent classification, CTR optimization, and funnel instrumentation. Experience with personalization at scale
- user modeling, preference inference, A/B experimentation, and behavioral analytics. Experience with platform infrastructure migrations (e.g. Heroku → cloud-native platforms) and tech stack modernization. Strong security mindset
- experience with data governance, access control, and security best practices for search and recommendation systems.
Our Engineering Values:
We hold ourselves to a high standard- because our partners and customers depend on us: Trust by default
- secure, accessible, performant, and scalable in everything we build. Engineering Commitment
- observability, performance, and security are first-class citizens, not afterthoughts. Curiosity & AI Fluency
- we fully embrace AI across our daily engineering chores, from code completion to production monitoring. Boldness
- we challenge the status quo and build the best-engineered solutions. Ownership
- we don't just ship features; we own the full lifecycle, including production.
- but to redefine what's possible
- for yourself, for AI, and the world.
Know your rights:
workplace discrimination is illegal. Any employee or potential employee will be assessed on the basis of merit, competence and qualifications- without regard to race, religion, color, national origin, sex, sexual orientation, gender expression or identity, transgender status, age, disability, veteran or marital status, political viewpoint, or other classifications protected by law.
Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Salesforce will consider for employment qualified applicants with arrest and conviction records. At Salesforce, we believe in equitable compensation practices that reflect the dynamic nature of labor markets across various regions. The typical base salary range for this position is $197,300
- $313,700 annually. In select cities within the San Francisco and New York City metropolitan area, the base salary range for this role is $237,700
- $344,700 annually.
Benefits
- 401(k) Plans
- Mental Health
- Employee Stock Options (ESOs)
- Health Insurance