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About This Role
*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
Employee Success
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 Agentic Workforce Strategy \& Innovation team sits at the intersection of two of the most consequential shifts in enterprise business: the rise of AI agents and the reimagination of how work gets done. We are looking for a Senior Manager of Strategic Modeling who functions as our Lead Architect for capacity. You will move the organization away from linear headcount tracking and toward a multi\-dimensional labor model that prioritizes value\-per\-task over cost\-per\-hire. This is a role for a quantitative strategist who sees the workforce not as a list of names, but as a dynamic system of human and digital capabilities.
What You'll Deliver
The Integrated Capacity Blueprint
- Own the design and evolution of the enterprise’s first integrated capacity model—one that treats human headcount and digital agents/AI as a single, optimizable system.
- Design and deploy a "Unified Labor Taxonomy" that allows for apples\-to\-apples performance and cost comparisons between human labor and AI agents.
- Establish the enterprise's first set of "Human\-AI Collaboration" benchmarks to guide executive\-level hiring and augmentation decisions.
Predictive Simulation \& Forecasting
- Build and maintain a 3\-year "What\-If" simulation engine that forecasts the impact of AI agent deployment on global headcount requirements across every major Business Unit.
- Translate abstract AI capability shifts (e.g., a breakthrough in autonomous reasoning) into concrete workforce supply and demand adjustments.
- Identify "Margin\-to\-Labor" calibration opportunities, pinpointing over\-provisioned human roles that should be augmented or transitioned to digital labor to protect enterprise margins.
Strategic Narrative \& Insight
- Distill complex capacity data into a clear leadership narrative; ensure the VP layer understands not just the "how many," but the "who, what, and where" of our future workforce.
- Identify and surface structural risks (e.g., talent shortages in key AI\-adjacent roles) before they impact our ability to execute the Innovation Roadmap.
Foundational Data Architecture
- Establish a unified "Workforce Data Cloud" that integrates employee, contingent, and digital worker information into a single source of truth for all strategic planning functions.
- Enable multi\-dimensional data slicing across organizational structures, job taxonomies, and cost centers, with the flexibility to incorporate new metrics before they are formalized in systems of record.
- Provide self\-service access through AI\-fronted interfaces (e.g., Tableau Agent), allowing stakeholders to query complex datasets using natural language to drive rapid analysis.
- Ensure high data integrity and validity to maintain executive trust, while allowing for seamless "drill\-down" capabilities from strategic hypotheses to individual record\-level details.
- Integrate external market intelligence (e.g., TalentNeuron, LinkedIn) to benchmark internal workforce costs, geography, and talent accessibility against global trends.
Your Toolkit: Skills That Will Drive Impact
Labor Economics \& Financial Rigor
- You have an expert\-level ability to model Total Cost of Ownership (TCO) for both human and digital resources, including the hidden costs of reskilling and technical debt.
- You understand how large enterprise budget cycles work and can defend a capacity model to a CFO with data\-backed confidence.
Quantitative Mastery
- You are fluent in the tools of modern data architecture—SQL, Python, or advanced BI platforms—and can wrangle fragmented task\-level data into clean, strategic inputs.
- You bring a "Product Mindset" to data, building models that are scalable, repeatable, and user\-friendly for non\-technical stakeholders.
Foresight \& Business Acumen
- You can "zoom out" to see how global AI trends affect our specific business model, and "zoom in" to see which specific teams are ripe for redesign.
- You move fast in ambiguous environments, making high\-judgment calls on data inputs even when "perfect" information doesn't exist.
How We Expect You to Use AI
- Model the behavior you're building toward. You are expected to be a daily, active user of AI tools—leveraging agents and assistants to manage your own workload and accelerate your modeling output.
- Agentic Modeling. You will leverage AI to run simulations on labor demand, identifying risks and opportunities that manual spreadsheets miss.
- Automated Data Synthesis. Use LLMs to ingest and summarize fragmented task\-level data from across the enterprise. You’re not just automating old work; you’re architecting new ways of seeing the workforce.
- 10x Efficiency. You are expected to use AI to automate 80% of routine data cleaning and report formatting, shifting your time entirely to high\-value strategic analysis and architectural design.
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.\&\#xa;\&\#xa;The typical base salary range for this position is $164,000 \- $261,500 annually. In select cities within the San Francisco and New York City metropolitan area, the base salary range for this role is $196,800 \- $285,300 annually.\&\#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-$285K 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. Director-level AI roles across all categories have a median of $244,288. This role's midpoint ($224K) sits 35% above the category median. Disclosed range: $164K to $285K.
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
AI roles in Chicago pay a median of $202,350 across 310 tracked positions. That's 10% above the national 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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