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About This Role
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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.
Salesforce is building the world's first Agentic Enterprise — and this role is part of the team laying the foundational infrastructure that makes agentic talent experiences possible. As Agents take on increasingly complex work, Salesforce's ability to match humans to high\-value tasks, verify capability at scale, and dynamically reskill its workforce depends on a robust Skills Capability Intelligence architecture. The Sr. Manager, Agentic Talent Capability \& Skills Strategy will execute and coordinate the foundational framework activities that power this shift — moving Salesforce from a static, job\-based skills model to a living, AI\-informed capability layer that fuels both human and agentic performance.
This role will report to the Senior Director, ATE Enterprise Skills \& Skilling and work alongside ES Product, Workforce Innovation, Talent Intelligence, Talent Management, and the Skills COE — all teams working in concert to build the talent infrastructure that underpins Salesforce's agentic transformation.
What You'll Own
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Capability Framework Execution \& Coordination
- Support the operationalization of the enterprise Skills Capability Framework — the foundational layer that defines how human capabilities are structured, verified, and surfaced in an agentic work environment.
- Coordinate with the Job Architecture team to ensure the Capability Framework aligns with enterprise job architecture and can support future human\-agent task allocation models.
- Participate in and help facilitate the cross\-functional Skills COE / Skills Guild — supporting governance on capability definitions, proficiency thresholds, and verification standards that inform both human skilling and agentic system inputs.
- Maintain documentation and data model consistency across ATE Innovation \& AI, ES Product, Blueprint, and Talent Intelligence team touchpoints — ensuring the capability layer remains reliable as the agentic product ecosystem evolves.
Skills to Capability Verification — Operational Delivery
- Execute the binary flag framework (inferred vs. verified states) and manage evidence trigger workflows (performance ratings, credentials, manager sign\-off, 360 signals) — the verification signals that determine whether a capability can be trusted by both human managers and agentic systems.
- Coordinate the four\-step verification workflow through to the verified (V) indicator on talent profiles — a data point that powers AI\-assisted talent matching, agent task routing, and reskilling recommendations.
- Support maintenance of Eightfold confidence threshold ( 0\.80\) standards in partnership with Workforce Intelligence and Talent Intelligence — ensuring the accuracy of inferred capabilities that feed agentic talent experiences.
- Support GTM coordination and enablement activities for loading capabilities into Career Connect, Salesforce's agentic career development platform.
Skills Graph \& Data Architecture — Support \& Coordination
- Coordinate with ES Product, Talent Intelligence, and Workforce Blueprint on Skills Graph operationalization activities — the underlying data infrastructure that enables agents to reason about workforce capability in real time.
- Support Phase 1 delivery: execute on the capability framework and contribute to building the foundational intelligence layer that will power AI\-driven talent decisions, agentic task matching, and human\-agent collaboration models.
- Partner with ATE Innovation \& AI on implementation tasks for capability flags in Eightfold — tracking evidence trail and systems write\-back progress to ensure capability data is available where agents and talent systems consume it.
Skills Integration into the Talent Value Chain — Support
- Coordinate activities to embed skills \& capabilities into the FY27 performance cycle touchpoints in support of senior leadership direction — anchoring human performance to the same capability standards that will inform agentic work allocation.
- Support skills\-based hiring integration activities with Talent TA — helping shift hiring from role\-based matching to capability\-based matching aligned with an agentic workforce model.
- Execute the Job Skills Validation process using established methodology — Work IQ data, SME surveys, and AI\-assisted analysis — to produce the verified capability data that agentic talent systems depend on; partner with Workforce Innovation Redesign on coordination.
Capability Proof\-of\-Concept \& Roadmap Tracking
- Execute Phase 1 capability proof\-of\-concept using the ESBP role as the template — coordinating capability definition, framework validation, and documentation to prove out the model that will scale across the enterprise's agentic talent infrastructure.
- Support scope alignment with ES Product on Career Connect capabilities, verification thresholds, and Blueprint boundaries — ensuring the POC produces outputs that are consumable by agentic talent platforms.
- Maintain and report on the Q2 capability roadmap; coordinate stakeholder updates across ES Product, Blueprint, Talent Intelligence, and the ATE LT to keep the agentic talent foundation on track.
Measurement \& Reporting
- Track and report against V2MOM measures including capability verification rates, Skills Graph adoption, and downstream talent outcomes — metrics that reflect the health of the agentic talent foundation.
- Compile and maintain business impact metrics data on productivity, reskilling, and skill\-to\-task matching velocity — demonstrating how a verified capability layer accelerates both human performance and agentic system effectiveness.
What You Bring
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- 5–7 years in talent strategy, workforce development, skills/capability coordination, or HR technology — with familiarity with skills intelligence platforms (Eightfold, Workday, Cornerstone, Lightcast or equivalent).
- Experience supporting or contributing to capability, competency, or ontological frameworks — connected to performance, technology systems, or AI\-driven talent platforms.
- Ability to operate at the intersection of people strategy and data/product delivery — you can translate between HR frameworks and the product and tech teams building agentic talent experiences.
- Strong organizational and project management skills with experience coordinating across matrixed teams in fast\-moving, AI\-transformation environments.
- Fluency with AI tools and a practitioner's mindset — you use AI to accelerate your own work and you understand how AI systems consume and act on structured data.
- Working knowledge of Skills Graph concepts, talent intelligence infrastructure, or skills data models — and genuine curiosity about how these power agentic workforce systems.
Why This Role
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This role sits at the execution center of Salesforce's transformation to an Agentic Enterprise. The work here isn't peripheral — it's the foundation. Agents can only route work, match humans to tasks, and drive reskilling recommendations if the underlying capability data is structured, verified, and trusted. The Skills Guild has launched, proficiency frameworks are defined, and the Skills validation process is mapped. This role is critical to operationalizing and sustaining the dynamic Capability \& Skills Intelligence infrastructure that makes AI\-driven, agentic talent experiences real. The work directly influences how 80,000\+ Salesforce employees grow, perform, and collaborate alongside agents in an AI\-first world.
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 .
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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 $143,400 \- $216,900 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 $143K-$216K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($180K) sits 18% below the category median. Disclosed range: $143K to $216K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Salesforce AI Hiring
Salesforce has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span CA, US, San Francisco, CA, US, Atlanta, GA, US. Compensation range: $194K - $344K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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