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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.
About the Role
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Salesforce is moving toward a future where AI agents do the engineering work, and that change starts with how we build software for ourselves. This role leads the AI transformation of the organization that builds and runs Salesforce's internal, employee\-facing technology \- and in doing so, creates the playbook every CIO in our customer base will need next.
Every enterprise we're selling to will hit exactly what we're hitting right now. Pilots worked. Agents are ready. The factory floor wasn't built for them. If we solve this operationally \- with receipts \- we own the playbook no competitor can replicate. We solve it for ourselves. Then we sell it for a decade.
Most of our engineers already use AI tools every day. The job now is to turn that broad adoption into one coordinated, measurable effort: clear standards, consistent adoption, and productivity gains that are real and visible across the whole organization \- running at agent speed, not human speed.
This is a senior leadership role that works largely through influence. You will partner across engineering, product, and design. Your impact comes from clarity, standards, and driving outcomes \- not from managing a large headcount.
What You'll Own
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Coordinate the AI effort. Bring the many separate AI and engineering initiatives across teams into one coordinated program. Reduce duplication, scale what works, and give the work a single, clear direction.
Standards and quality. Own the standards for how teams build with AI, and the process for improving them. Define what good looks like, how work is reviewed, and how expectations rise as the tools mature. Refuse to accept output that doesn't work beautifully for the end user.
Tools and adoption. Choose the set of AI development tools the organization uses \- Cursor, Claude, Agentforce, and what comes next \- and define the level of adoption every team is expected to reach. Keep the organization current with the best tools available.
Delivery and release. Remove the deployment, testing, and process bottlenecks that hold back the productivity AI makes possible. Modernize the path from idea to production so teams can ship in weeks, not quarters.
Measurement. Measure how work moves from idea to production and how the tools are used. Build clear, role\-specific measures so impact is comparable across the org. Track what matters \- frequency of ideas tested, outcomes learned, adoption \- and avoid metrics that look impressive but don't reflect real impact.
Builder progression. Define and drive the system that makes every build compoundingly faster than the last: shared context files, command libraries, three\-model review workflows, and postmortem culture. The builders who win aren't the ones who use AI \- they're the ones who've built the system around AI.
Roles and ways of working. Define how engineering, product, and design roles change as agents take on more of the work, and shape the broader way the organization operates around it \- including how we move toward a headless model where agents run the work and humans make the judgment calls.
You're Our Person If...
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You ship, you don't just coordinate. You have a working prototype in your hands before you have a deck. When a decision takes more than 48 hours, you shrink the question, test it, and get the data.
Speed is your default operating mode. You think in agent speed, not quarterly planning cycles. When the build loop runs faster, the org's ability to learn and course\-correct moves faster too.
Outcomes are the only score. Adoption and shipped value are what count. Roadmaps, status decks, and committee approvals don't impress you. Fast failure is data, not identity.
You own it. One empowered owner who makes fast decisions, works in the open, and knocks down anything that slows down learning. You make the teams around you better without needing a large team of your own.
You work in the open. Public channels, public demos. You assume someone has already worked on this problem \- you find them and build with them, not around them.
You have strong product taste. You know what makes a tool genuinely intuitive and useful, and you refuse to ship things that don't look, feel, or work beautifully for the end user.
You've led at scale and built with your hands. You have 12\+ years in engineering, product, or transformation leadership \- and you can still sit down and build. Real technical credibility that earns the trust of senior engineers quickly, not just titles that imply it.
What You Bring
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- 12\+ years in engineering, product, or transformation leadership, ideally in a large technology organization.
- Deep technical credibility. You have led engineering or platform teams and earn the trust of senior engineers quickly \- and you've shipped real things, not just managed people who did.
- Hands\-on fluency with modern AI development tools: Cursor, Claude, Agentforce, Codex, or equivalent. A clear, evidence\-based view of where productivity gains actually come from \- not just awareness of the category.
- Demonstrated understanding of agentic architecture: model routing, harness engineering, context\-setting, and the governance and trust layer that makes orchestration reliable at scale.
- A track record of driving change at scale: new standards, new ways of working, and adoption that lasts.
- Strong instincts for measurement. You track what matters \- frequency of ideas tested, outcomes learned, adoption rates \- and avoid metrics that look impressive but don't reflect real impact.
- Excellent communication. You can write a clear executive brief, run a compelling public demo, and bring a room into agreement.
- Comfort with ambiguity, low ego, and high ownership.
Even Better If...
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- You've built, deployed, or scaled agentic AI or automation tools inside a large enterprise engineering org.
- You know Salesforce products deeply, either from working here or as a power user elsewhere.
- You've defined and shipped a builder progression system \- shared context files, command libraries, postmortem culture \- that made a team measurably faster over time.
- You've worked in a model where Slack is the operating layer: async\-first, public by default, documentation\-as\-you\-go.
- You bring an existing network in enterprise engineering, platform, or AI transformation communities.
Why This Role Matters
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The tools are proven and adoption is already real. What is missing is the coordination that turns hundreds of separate efforts into one lasting change \- running at agent speed, not human speed.
Twelve months from now, the best teams in this org won't be managing feature backlogs. They'll be running agentic systems that handle work autonomously \- humans in the loop only for judgment calls.
That future doesn't happen on its own. It needs someone who can set the standard, build the system, and bring the whole organization with them. That's this role \- and what we build here becomes the field guide for every enterprise customer asking "our pilots worked, now what?"
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 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. Director-level AI roles across all categories have a median of $272,150.
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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