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About This Role
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Job Category
Sales
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.
Principal Solution Engineer \- Data \& Integration Specialist \- Federal Civilian
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Location: Washington D.C. / MD / VA / Remote
Description
Salesforce is hiring a Data \& Integration Specialist (DIS) to join our Global Public Sector Solution Engineering team. The AI revolution is a Data evolution and this critical role is at the forefront of making AI a reality for our customers. As a DIS, you are more than a technical expert; you are a strategic architectural partner who consolidates expertise across MuleSoft (Integration), Tableau (Analytics), and Data 360 (Unified Data Management) to solve the most complex data challenges in government.
You will serve as the single point of contact for sales teams, ensuring every data opportunity is handled by a specialist who thinks like an architect and communicates outcomes and impacts in the voice of the business user. You will be a vital contributor to the success of our agency customers by conducting holistic discovery, reframing the "data journey," and designing compliant, AI\-ready architectures that deliver actionable insights from disparate silos.
In this role, you will work with Federal Civilian agencies and bridge the gap between legacy system connectivity and modern mission outcomes, acting as a trusted advisor who can navigate everything from API management to real\-time data unification and advanced analytics.
### What you get working for the Data \& Integration SE Team:
- A Unified Mission: Work in a specialized team that eliminates fragmented resource requests by owning the entire data lifecycle.
- Outcome\-First Leadership: Opportunities to lead high\-impact discovery sessions that start with agency mission outcomes (e.g., citizen services, public safety) rather than product features.
- Strategic Growth: Support from leaders committed to your mastery of enterprise data architecture and designing data strategies that leverage MuleSoft, Tableau, Data 360, and Informatica
- Innovation at Scale: Work for the \#1 AI CRM company, building the trusted data foundations required for the next generation of Agentforce and autonomous AI agents.
### Qualifications you’ll need to be successful:
- Architectural Mindset: Ability to design holistic, composable architectures that scale securely in highly regulated public sector environments.
- Multi\-Platform Technical Depth: Hands\-on experience or deep conceptual knowledge of Integration (MuleSoft), Data Analytics (Tableau), and Big Data/CDP (Data 360\). A qualified candidate will have expertise in no less than 2 of these capabilities, a great candidate will have expertise in all three.
- Strategic Communication: Excellent ability to reframe technical challenges into business value for C\-level government executives and technical stakeholders alike.
- AI Readiness: Understanding of the value of "Trusted Context Foundations" for AI, including data grounding, RAG (Retrieval\-Augmented Generation), and metadata intelligence.
- Collaboration \& Influence: Proven ability to partner early with Account Executives to drive sales strategy and lead "Data Discovery" from day one.
- Experience: Minimum of 7 years of professional experience in Solution Engineering, Enterprise Architecture, or Digital Transformation.
- Technical Foundations: Experience with database design, Java, APIs, and cloud infrastructure (AWS, Azure, GCP) is preferred.
### Preferred Qualifications:
- Certifications: One of more \- MuleSoft Certified Architect, Tableau Desktop/Server Specialist, or Salesforce Data Cloud Consultant.
- Industry Knowledge: Deep understanding of Public Sector compliance (FedRAMP) and common government data silos.
- Ecosystem Expertise: Familiarity with Informatica for advanced data governance and MDM.
### What you’ll achieve:
3 months:
- Master the Stack: Complete intensive Salesforce Trailhead courses and certification paths for MuleSoft Anypoint Platform, Data 360, and Tableau.
- Shadow \& Learn: Join active Public Sector engagements to observe how DIS leads discovery and architects multi\-product solutions.
- Build Your Point of View: Align with the GTM Plan and begin building your "Data Journey" whiteboarding skills.
12 months:
- Own the Technical Win: Serve as the lead architect on complex, multi\-cloud data opportunities, delivering higher win rates and faster deal cycles.
- Evangelize the Vision: Execute high\-impact architectural workshops and "Stand \& Deliver" sessions that showcase the power of unified data.
- Enable the Ecosystem: Mentor account teams and partners on how to position "Data \+ Integration" as the foundation for AI.
Drive Mission Outcomes: Successfully deploy architectures that unify citizen data, modernize legacy systems, and provide real\-time dashboards for mission operations.
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.
At Salesforce, we believe in equitable compensation practices that reflect the dynamic nature of labor markets across various regions.\&\#xa;\&\#xa;The typical base salary range for this position is $164,000 \- $210,910 annually\&\#xa;\&\#xa;There is a different range applicable to specific work locations. In California and New York, and select cities in the metropolitan areas of Boston, Chicago, Seattle, and Washington DC, the base pay range for this role in those locations is $196,800 \- $231,980 per year. Your recruiter can share more about the specific salary range for the job location during the hiring process.\&\#xa;\&\#xa;The range represents base salary only, and does not include company bonus, incentive for sales roles, equity or benefits, as applicable.
Salary Context
This $164K-$231K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 roles with salary data).
View full AI/ML Engineer salary data →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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% 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 $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($197K) sits 19% above the category median. Disclosed range: $164K to $231K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Salesforce AI Hiring
Salesforce has 19 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect, AI Software Engineer. Positions span San Francisco, CA, US, New York, NY, US, Chicago, IL, US. Compensation range: $155K - $451K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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