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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 — it’s a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all.
Ready to level-up your career at the company leading workforce transformation in the agentic era? You’re in the right place! Agentforce is the future of AI, and you are the future of Salesforce.
Engineering at Qualified
Engineering at Qualified is a high-ownership, high-velocity environment where engineers ship customer-facing product to real enterprises at scale. We're building at the frontier of AI + product: developing agentic software and
embedding AI directly into our platform, using modern toolchains and processes that help engineers move faster while staying focused on high-impact problem solving.
Following our acquisition by Salesforce, we continue to operate with the pace and ownership of a startup, backed by the distribution, resources, and long-term runway of a global enterprise. Engineers have true end-to-end ownership from idea to production: scoping, implementation, testing, deployment, monitoring, and iterative improvement based on real customer feedback.
You'll work alongside a high-talent-density team that cares deeply about craft, performance, reliability, and user experience, and that raises the bar through collaboration, code review, and strong engineering principles. You'll make architecture decisions, solve meaningful system design challenges, and see your work land quickly with customers, without layers of approvals slowing you down.
The Role
As an Engineer on the AI Core team, you'll operate as a technical leader who turns ambiguous, high-stakes AI problems into durable, high-quality systems, while raising the technical bar for the broader team. AI Core builds the retrieval systems and agent infrastructure that power how our AI agents find, trust, and use knowledge, and the developer tooling that lets engineers across the company ship production agents quickly and safely.
You'll lead significant initiatives end to end: faithful answering over a customer's own knowledge, retrieval that combines RAG and hybrid search, content lifecycle controls such as freshness, deduplication and source precedence, transcript processing, and the frameworks, registries, and evaluation tooling that make building a production agent a paved path. You'll partner closely with product, design, and engineering leadership to decide what we build, how we build it, and how we measure whether answers actually got better.
What You'll Do
Lead retrieval initiatives end to end: Design, build, and operate retrieval systems using RAG and hybrid search (lexical plus vector, reranking, filtering) so agents answer from the right content with the right amount of context. Own delivery from scope and design through launch, monitoring, and iteration.
Build developer tooling for production agents: Drive technical design for the frameworks and platform pieces our engineers use to build agents, including multi-agent orchestration, shared primitives for prompts, tools, tracing, and evaluation.
Own answer quality: Diagnose why answers are thin, wrong, or inconsistent by separating retrieval problems from generation problems (prompt, token budget, model choice). Set a quality bar we can test against, and lead the
work to reach it.
Design content and data models: Define how content from documents,
knowledge bases, and call transcripts is ingested, segmented, labeled, and retrieved, including the primitives behind freshness, dedupe, source
precedence, and internal versus external visibility.
Make AI changes safe to ship: Build evaluation, observability, and rollout
practices (offline evals, online monitoring, regression detection) so we can change prompts, models, and retrieval with confidence. Evaluate multiple
solution paths and know when to trust, redirect, or override model and agent output.
Set performance and scaling benchmarks across the platform we own:
Define and track targets for retrieval and indexing, LLM provider usage, and agent orchestration.
Own operational health: Ensure the reliability and performance of the systems your team owns. Participate in on-call, lead incident resolution for your area, and communicate clearly with the team and stakeholders during incidents.
Use agentic development to raise leverage: Use coding agents by default for implementation, testing, refactoring, and documentation while holding a high validation bar. Create reusable workflows, patterns, and tooling that raise the effectiveness of your team beyond your own output.
Partner across functions: Work with product, design, and engineering
leadership to shape roadmap, tradeoffs, and requirements, and to translate strategy into concrete execution plans.
Raise team effectiveness: Mentor engineers, give high-quality code review and technical guidance, and improve standards, tooling, and processes.
Who You Are
Proven experience shipping production AI or backend systems in a high ownership environment, with a recent focus on LLM-powered applications.
Expertise building retrieval systems using RAG and hybrid search, including chunking and indexing strategy, embeddings, vector and lexical search,
reranking, and measuring retrieval quality.
Expertise building developer tooling that lets developers build production agents, which can include frameworks for multi-agent orchestration, an agent and tool registry, and shared infrastructure for tracing and evaluation.
Experience owning platform initiatives from design through adoption,
across systems such as retrieval and indexing, LLM provider integrations, and agent orchestration, including driving adoption and evolving the platform as usage grows.
Demonstrated technical leadership: you lead significant projects, design solutions for non-trivial problems by weighing tradeoffs, and influence architecture and execution beyond your immediate workstream.
Strong track record delivering complex projects with multiple stakeholders, dependencies, or phases.
Strong backend engineering fundamentals and proficiency in languages such as Ruby on Rails, Python, JavaScript, or Go, with SQL and RDBMS experience (PostgreSQL preferred).
Hands-on experience with the AI application lifecycle: experiment setup, dataset curation, offline evaluation, error analysis, deployment, and online evaluation.
Comfortable operating in ambiguity and driving clarity: you can frame problems, propose solutions, and align others.
Strong communicator across functions, able to explain technical ideas to both technical and non-technical audiences and to translate complex concepts into business impact.
Ownership mindset and bias for action. You raise the bar for the team through mentoring, code review, and your own work.
Curious and continuously learning, with an agent-forward approach to how you build software.
Nice to Have
Experience with pgvector or other vector indexes, and with query performance at scale.
Experience with content ingestion from third-party systems (document stores, knowledge bases, CRM knowledge objects) and the lifecycle problems that follow: sync state, dedupe, source conflict.
Experience processing conversation or call transcripts, including handling PII and consent constraints.
Experience designing evaluation suites that score qualities such as thoroughness and faithfulness, not only correctness.
Experience with voice or real-time conversational agents.
Experience with enterprise SaaS (especially B2B marketing or sales tech) or Salesforce/CRM integrations.
Startup experience and rapid shipping.
Why Join Qualified
Foundational impact: Shape how our AI agents retrieve, reason, and answer, and the tooling every engineer here uses to build them.
Lead real initiatives: Own significant projects end to end, with scope and
influence that grow as you deliver. You'll own the platform as it scales, from retrieval to model providers to agent orchestration, and set the performance and reliability bar that grows with the company.
AI as a core product surface: Turn new AI capabilities into dependable
experiences that customers trust, with strong engineering around evaluation, observability, and iteration.
High bar, low bureaucracy: Work with a team that values craft and decisive execution, where strong ideas win and shipping is the default.
Room to grow: Take on larger technical and product scope over time: leading projects, influencing architecture, and leveling up quickly.
If you're excited to build retrieval systems and agent infrastructure at the intersection of AI and enterprise software, we'd love to hear from you.
Unleash Your Potential
When you join Salesforce, you’ll be limitless in all areas of your life. Our benefits and resources support you to find balance and be your best , and our AI agents accelerate your impact so you can do your best . Together, we’ll bring the power of Agentforce to organizations of all sizes and deliver amazing experiences that customers love. Apply today to not only shape the future — but to redefine what’s possible — for yourself, for AI, and the world.
Accommodations
If you need a reasonable accommodation during the application or the recruiting process, please submit a request via this Accommodations Request Form .
Please note that Salesforce uses artificial intelligence (AI) tools to help our recruiters assess and evaluate candidates’ resumes and qualifications throughout the recruiting process. Humans will always make any candidate selection and hiring decisions. Please see our Candidate Privacy Statement for more information about how we use your personal data and your rights, including with regard to use of AI tools and opt out options.
Posting Statement
Salesforce is an equal opportunity employer and maintains a policy of non-discrimination with all employees and applicants for employment. What does that mean exactly? It means that at Salesforce, we believe in equality for all. And we believe we can lead the path to equality in part by creating a workplace that’s inclusive, and free from discrimination. 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. This policy applies to current and prospective employees, no matter where they are in their Salesforce employment journey. It also applies to recruiting, hiring, job assignment, compensation, promotion, benefits, training, assessment of job performance, discipline, termination, and everything in between. Recruiting, hiring, and promotion decisions at Salesforce are fair and based on merit. The same goes for compensation, benefits, promotions, transfers, reduction in workforce, recall, training, and education.
In the United States, compensation offered will be determined by factors such as location, job level, job-related knowledge, skills, and experience. Certain roles may be eligible for incentive compensation, equity, and benefits. Salesforce offers a variety of benefits to help you live well including: time off programs, medical, dental, vision, mental health support, paid parental leave, life and disability insurance, 401(k), and an employee stock purchasing program. More details about company benefits can be found at the following link: https://www.salesforcebenefits.com.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 $172,500 - $260,100 annually. In select cities within the San Francisco and New York City metropolitan area, the base salary range for this role is $207,800 - $285,800 annually.

The range represents base salary only, and does not include company bonus, incentive for sales roles, equity or benefits, as applicable.
Salary Context
This $172K-$285K range is above the 75th percentile for AI Software Engineer roles in our dataset (median: $194K across 269 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 11,520 AI roles we're tracking, AI Software Engineer positions make up 5% of the market. At Salesforce, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $220,000 based on 1,688 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. Disclosed range: $172K to $285K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Salesforce AI Hiring
Salesforce has 22 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Architect, Data Scientist. Positions span San Francisco, CA, US, New York, NY, US, New York, NY. Compensation range: $217K - $426K.
Remote Work Context
Remote AI roles pay a median of $188,200 across 2,430 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (140) are outnumbered by mid-level (5,630) and senior (4,176) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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