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About This Role
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Job Category
Enterprise Technology & Infrastructure 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.
Sr. Director, Agentic Transformation - DET Org
Salesforce is looking for a consultative, technically credible Sr. Director, Agentic Transformation to lead how the Chief Operating & Financial Officer (COFO) organization uses data and agentic AI to enable Finance, Controllership. Reporting to executive leadership in the Digital and Emerging Technology (DET) organization, you will be the product and transformation leader who turns fragmented data into trusted, governed data products. You will then build agentic workflows on top of them to speed up close, improve forecast accuracy, strengthen controls, and remove manual effort from finance operations.
You will identify jobs to be agentified and find and fix data and system gaps to enable transformation, with a technical lens and the executive presence to land change with functional VPs in COFO. We are Customer Zero, the best example of Salesforce products delivered globally, at scale, sustainably.
Key Responsibilities & Impact Areas
- Agentic Product Roadmap: Own the product vision, roadmap and backlog for agentic solutions across Finance, Controllership, Legal and Employee Services. Prioritize by value, feasibility and risk, and take solutions from concept through build, launch and scale.
- Rapid Innovation Squads: Lead and staff Rapid Innovation Squads, small cross-functional teams that move quickly from problem definition to a working agent. Set the squad charter, scope and success criteria, and partner with builders across engineering and data solution to deliver in value increments.
- Value Delivery: Be accountable for outcomes, not output. Define the value case for each initiative up front, track it through delivery, and confirm it is realized after launch. Report results transparently to leadership.
- Navigating Ambiguity: Take on undefined, first-of-their-kind programs where the problem, the data and the right solution are not yet clear. Break them into testable hypotheses, make decisions with incomplete information, and adjust as you learn.
- Agentification of Work: Break down end-to-end processes into jobs and tasks, and decide which should be handled by agents, which need a human in the loop, and which should be redesigned or retired. Build agents that work with real finance and operations data and the controls around them.
- Data Foundations for Agents: Partner with data and engineering teams to turn fragmented data into governed, reusable data products with clear definitions, lineage and ownership, so agents can be trusted in production.
- “In the Seams” Innovation and Efficiencies: Identify and bridge efficiency gaps where traditional functional ownership overlaps, and ensure new technologies integrate cleanly into the native Salesforce product ecosystem and existing systems of record.
- Customer Zero: Run our own products internally first. Feed product feedback and field-tested patterns back to Salesforce product teams, and turn what works into a repeatable model.
- Enterprise Adoption & Scaling: Partner with cross-functional leaders to scale agentic solutions, drive adoption, and eliminate operational friction across functional silos.
- Agentic ROI & Efficiency Benchmarking: Establish rigorous baselines and performance frameworks to measure productivity gains and ROI, with data-driven visibility into the efficiency captured across the organization.
- Executive Advisory: Serve as a credible partner to COFO and DET leadership, providing data-driven recommendations on where and how to apply AI-led operations.
Required Qualifications
- Product Leadership: 8-10 years of product management, product strategy or technical program leadership experience, with proven ownership of product roadmaps in technology organizations.
- Transformation Leadership: Proven track record of leading teams (20+) to deliver complex, cross-functional transformations for multi-billion-dollar SaaS and technology organizations.
- AI & Data Expertise: Deep technical experience building products and processes with GenAI, agentic AI and broader data solutions, taking them from prototype to production.
- Value Orientation: Demonstrated success defining, measuring and delivering business value, with evidence of outcomes achieved.
- Comfort with Ambiguity: Track record of driving ill-defined, fast-moving programs to results, with sound judgment under uncertainty and a bias toward testing and learning.
- Consultative Credibility: Exceptional ability to influence outcomes at the VP and executive level, with experience acting as a key product lead.
Preferred Qualifications
- Familiarity with Salesforce’s product ecosystem and its application in driving internal business transformation.
- Experience with enterprise operations processes.
- Experience leading rapid, small-team innovation or incubation efforts.
- Proven ability to translate technical talk into business value for non-technical executive audiences, and vice versa.
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.

The typical base salary range for this position is $218,400 - $365,200 annually. In select cities within the San Francisco and New York City metropolitan area, the base salary range for this role is $263,200 - $401,400 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 $218K-$401K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 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 $212,040 based on 13,780 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,250. This role's midpoint ($309K) sits 46% above the category median. Disclosed range: $218K to $401K.
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.
Location Context
AI roles in New York pay a median of $221,000 across 3,257 tracked positions. That's 4% 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 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).
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 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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