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About This Role
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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 — 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.
The Experience
We're looking for a Senior Solution Architect who operates at the intersection of deep business process intuition and hands-on agentic engineering. This is a senior individual contributor role operating at director level — you will be a hands-on technical leader who builds, ships, and influences at scale without relying on organizational authority. You will not have direct reports, but your impact will be felt across an architecture organization. You think strategically and execute rapidly, often in the same week.
The core mandate of this role is incremental transformation of prospect management — from lead generation and qualification through lead management and opportunity progression. This pipeline was architected for human-scale workflows, and the business expectation is clear: rapidly layer in new agentic tooling (Salesforce on Salesforce), measure impact, innovate, and iterate. You won't be rebuilding from scratch — you'll be threading intelligent agents into a live, complex system while keeping it moving. That requires as much judgment as it does technical skill. Wherever there is an opportunity to apply agentic AI to transform a business process, your first question on every engagement is always: does this process make sense before we agentify it?
You'll bring coherence to a multi-technology landscape where multiple agentic tools are being implemented simultaneously — ensuring that buyer engagement agents, conversational sales agents, and the underlying CRM and routing infrastructure form a coherent, observable, and scalable whole.
This role sits at the intersection of three organizational pillars — Sales Technology, Digital Marketing, and Data Solutions — and operates as a key architectural partner across all three. You will be accountable for E2E outcomes while holding direct responsibility for the architectural and technology components that live in CRM. Strong cross-pillar collaboration is not optional; it's how this role delivers.
This role oscillates between two modes. In fast-twitch mode, you're supporting small, focused squads — simplifying the process, and getting a working prototype in the hands of users within days, making key architectural decisions that set the squad up for delivering a successful business outcome. You're not waiting for alignment; you're generating it through working software. In slow-burn mode, you're providing early architectural points of view on complex enterprise systems that don't fit the fast-twitch pace model — influencing the direction of large, integrated programs where the cost of getting the architecture wrong compounds over time. Strategic thinking and rapid execution are both required; this role demands you bring both, calibrated to the situation.
You'll work across initiatives of varying scale and complexity — from focused two-week sprints to multi-quarter enterprise programs — and your job is to bring the right architectural posture to each one. Speed of learning and speed of shipping are equally important here — rapid learning is what makes rapid delivery sustainable.
Equally important: this role advances how we build. You'll practice and model an AI-native development lifecycle (AIDLC) — using Cursor, Claude Code, and Codex not as productivity shortcuts but as a fundamentally different way of working. Architecture patterns, best practices, and compliance checks should be baked into the tooling, not left to individual teams to figure out.
What You’ll Actually Be Doing...
Agentic Architecture & Incremental Transformation
- Own the architectural foundation for agentic selling across the full marketing-to-sales pipeline — lead generation, lead qualification, lead management, and opportunity progression
- Drive an incremental transformation approach: layer agentic tooling into live systems deliberately, instrument for impact, and create tight feedback loops for innovation and iteration
- Define and track meaningful architectural success metrics — including agent-driven pipeline conversion lift, lead qualification rates, routing accuracy, and agent deflection/escalation patterns
- Assess and rationalize the current multi-technology landscape, identifying conflicts, dependencies, and integration gaps
- Design multi-agent orchestration patterns that are coherent, observable, and production-grade — not prototypes layered on existing systems
- Ensure lead routing, territory assignment, and CRM workflows are refactored — incrementally — to support agent-scale throughput and decision-making
- Lead context engineering practices — designing the information architecture, retrieval strategies, and grounding mechanisms that give agents the right context to act reliably and accurately
Define agent lifecycle management practices: design, testing, iteration, observability, and governance from prototype through production *
Agentic Builder — Design & Build
- Assess and simplify business processes before applying technology; show the business a working prototype within days on fast-twitch engagements
- Develop and Apply agentic architecture patterns that apply across the the full Prospect-to-Cash domain
- Design context engineering strategies — information architecture, grounding, memory patterns, and retrieval design — that give agents the right context to act reliably
- Define agent lifecycle practices: design, testing, iteration, observability, and governance from prototype through production
Codify architecture patterns and best practices directly into tooling so they travel with the work, not sit in a document *
Forward-Looking Architecture
- Serve as an early architectural point of view across multiple initiatives, technology, product innovations. Serve as a trusted advisor to the Product, Engineering organization as well as Senior Leadership to navigate through ambiguity.
- Engage across initiatives of all scales, bringing the right level of architectural rigor to each — from rapid prototyping engagements to complex, multi-system programs
- For complex enterprise programs, break down intake requests to achieve incremental outcomes, minimize dependencies, and reduce downstream cost and risk — this is where judgment about what not to build matters as much as what to build
- Identify and escalate process or data blockers that will slow agentic transformation; make them visible rather than working around them
Act as connective tissue between fast-moving squads and the domain teams that own core business technology — ensuring fast-moving work lands on solid architectural ground *
Strategic Influence, Rapid Validation
- Approach every engagement with a strategic lens — understand the business outcome before proposing a technical direction
- Validate with the business first, use AI to generate evidence of value quickly, then scale
- Accelerate business analysis and decision-making by applying AI — reducing the time between a question and a validated answer from weeks to days
- Work with one empowered business decision-maker per engagement, not a committee
Influence enterprise architecture direction on complex programs where the fast-twitch model isn't appropriate, without losing the bias for action *
AI-Native Ways of Working (AIDLC)
- Practice AIDLC daily — Cursor, Claude Code, and Codex are your default toolchain, not optional accelerators
- Help establish AIDLC standards across the broader engineering team: AI-assisted architecture review, AI-generated documentation, AI-augmented testing, agent-assisted delivery workflows
- Codify best practices into shared context files, spec markdowns, and harness configuration — source-controlled and accessible to the whole team
Make your work discoverable and reusable by default; contribute to knowledge sharing across squads continuously *
Process-First, Business-Embedded
- Sit close enough to users and business partners to feel what they feel — user empathy is a prerequisite, not a nice-to-have
- Assess and challenge underlying business processes before agentifying them — simplification is often more valuable than automation
- Participate in outcome-based accountability cadences — be prepared to answer: what did the business get from their investment this week?
Treat failure as data: take intentional action, learn fast, and adjust *
Enterprise Architecture Influence
- Provide architectural POVs on complex, highly integrated programs where fast-twitch pace isn't appropriate
- Apply AI to accelerate business analysis and decision-making even when the delivery model is more traditional
- Ensure agentic components being built on fast-twitch engagements are architecturally compatible with core enterprise systems they'll eventually integrate with
Influence data architecture and integration strategy across Salesforce Sales Cloud, Agentforce, Data Cloud, and Snowflake *
You’re Our Person If...
- 10+ years of software engineering experience, with 5+ years building Salesforce solutions at scale
- Builder-architect mindset — you design and build in the same iteration; you are comfortable owning both the POV and the proof
- Proven ability to assess and simplify business processes before applying technology — process-first is non-negotiable
- Broad Prospect-to-Cash domain knowledge preferred— pattern recognition across lead management, opportunity management, quoting, ordering, and renewals; you don't need to be a deep expert in every area, but you need to plug in quickly and credibly
- Demonstrated learning agility — a track record of coming up to speed rapidly on both unfamiliar business domains and new technical paradigms; the ability to go from zero context to credible POV in days, not weeks
- Strong proficiency in agentic architecture: multi-agent orchestration, context engineering, LLM integration, agent lifecycle management
- Hands-on experience with AI-native development tooling (Cursor, Claude Code, Codex) — practiced, not theoretical
- Strategic thinker who executes — comfortable holding a long-term POV while delivering working software in days
- Comfort operating across initiative scales — from focused two-week sprints to multi-quarter enterprise programs
- Ability to codify patterns into tooling, not just documentation
- Strong proficiency in Salesforce platform: Sales Cloud, Agentforce, Data Cloud
- Expertise in software architecture patterns: microservices, event-driven, distributed systems
- Exceptional communication — equally fluent with engineers and business partners
High agency, low ego — failure is data, not identity *
Even Better If...
- Experience building large-scale Salesforce implementations spanning multiple clouds and integration layers — including data modeling, security model design, and cross-org architecture
- MuleSoft — experience designing integration architectures for P2C data flows (lead handoffs, order sync, entitlement propagation) using API-led connectivity and event-driven patterns
- Agentforce platform depth — hands-on experience building custom agents using Agent Builder, defining agent topics and actions, grounding agents with Data Cloud Retrieval Augmented Generation (RAG), and deploying agents across Sales Cloud surfaces (Einstein Copilot, Service Cloud, Slack)
- Data Cloud — experience modeling unified customer profiles (Individual, Contact Point, Engagement), building calculated insights and segmentation for agent grounding, and configuring Data Cloud activations that feed downstream agent actions
- Knowledge of best practices for MCP (Model Context Protocol) and A2A (Agent-to-Agent) development for agent interoperability — including how to expose Salesforce data and actions as MCP tools consumable by external orchestrators
- Experience instrumenting agent observability: structured logging of agent reasoning traces, tool call latency, hallucination detection, and feedback loop design for continuous prompt iteration
- Experience with buyer engagement or conversational intelligence platforms (e.g., Qualified, Gong, Chorus) and how they integrate with Salesforce as data sources for agent context
- Public cloud experience (AWS preferred) — particularly Lambda, API Gateway, Bedrock, or S3 in the context of hosting agent tools, context stores, or retrieval backends that extend Salesforce agents
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
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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.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Salesforce, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Salesforce AI Hiring
Salesforce has 15 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Software Engineer, Research Engineer. Positions span Bellevue, WA, San Francisco, CA, US, Indianapolis, IN, US. Compensation range: $223K - $344K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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