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About This Role
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Job Category
Customer 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 AI Innovation Lead– Life Sciences is responsible for defining and accelerating the AI-enabled delivery strategy for Salesforce's global Life Sciences Professional Services business. This executive will serve as the strategic bridge between Professional Services, Customer Success, Product Management, Engineering, and Global Delivery, ensuring that customer implementation experience directly influences product evolution, AI innovation, and delivery transformation.
Working across more than 50 global Life Sciences customer implementations today, with significant growth expected over the next several years, this leader will identify implementation patterns, prioritize innovation investments, and help shape the next generation of industry-specific AI capabilities, reusable assets, and delivery accelerators.
Leading a global team, this role will help transform delivery intelligence into scalable solutions that improve customer outcomes, accelerate AI adoption, and enable consistent, high-quality delivery across the Life Sciences industry.
Key Responsibilities
Industry Delivery Intelligence
- Develop a comprehensive view of implementation trends, customer challenges, and delivery opportunities across Salesforce's global Life Sciences portfolio.
- Identify common patterns across customer engagements and translate those insights into strategic opportunities for AI, automation, reusable assets, implementation accelerators, and delivery innovation.
- Establish a continuous feedback model that transforms implementation experience into scalable delivery capabilities.
Agentic Delivery Strategy
- Define the roadmap for Life Sciences-specific agentic delivery capabilities that improve implementation quality, delivery productivity, and customer outcomes.
- Prioritize the AI assets, accelerators, reusable frameworks, and implementation capabilities needed to bridge the gap between current product capabilities and customer demand.
- Recommend strategic investment priorities for industry-specific AI innovation and partner across Professional Services Technology to bring those capabilities to market.
Product & Customer Partnership
- Serve as the primary liaison between Global Professional Services, Customer Success, and Product Management for Life Sciences.
- Capture customer and delivery feedback and translate it into actionable product enhancements, roadmap priorities, and implementation best practices.
- Partner closely with Product Engineering to ensure implementation insights directly influence product strategy and future platform capabilities.
Cross Functional Leadership
- Serve as the executive subject matter expert for Agentic Transformation within the Life Sciences industry.
- Partner closely with Professional Services Delivery, Customer Success, Product, Global Delivery Centers, Agentic Innovation, Global Tools & Productivity Engineering, and Global Technology Foundations & Architecture to accelerate AI adoption across Life Sciences engagements.
- Help align industry priorities across delivery methodology, AI investments, reusable assets, and product innovation.
Industry Innovation Portfolio
- Define and prioritize the portfolio of Life Sciences-specific AI assets, implementation accelerators, reusable delivery patterns, and agentic solutions.
- Partner with the ProServ Agentic Innovation Lab and engineering teams to convert customer insights into scalable technical capabilities.
- Establish governance to measure adoption, business impact, customer outcomes, and value realization across the Life Sciences innovation portfolio.
Organizational Leadership
- Lead and develop a high-performing global team of approximately 20 professionals focused on industry innovation, architecture, delivery excellence, and AI enablement.
- Foster a culture of innovation, customer obsession, continuous learning, and AI-first thinking.
- Drive measurable improvements in customer success, delivery productivity, AI adoption, and operational excellence.
Qualifications:
Required
- 15+ years of leadership experience in Professional Services, enterprise software, digital transformation, Life Sciences consulting, or technology organizations.
- Deep understanding of the Life Sciences industry, enterprise implementation programs, and customer transformation initiatives.
- Proven experience partnering across Product Management, Engineering, Professional Services, Customer Success, and executive stakeholders.
- Demonstrated success translating customer needs into product strategy, technology investments, reusable assets, or delivery innovation.
- Experience leading global, cross-functional organizations and influencing without direct authority.
- Strong executive communication, strategic thinking, and stakeholder management skills.
Preferred
- Experience with AI-enabled delivery models, Agentforce, agentic technologies, automation platforms, or enterprise AI solutions.
- Salesforce Life Sciences Cloud or broader Salesforce ecosystem experience.
- Experience building industry accelerators, reusable implementation assets, or customer success frameworks.
- Background in healthcare, pharmaceuticals, medical devices, biotechnology, or regulated industries.
Leadership Expectations
- Champion AI-enabled ways of working and accelerate the adoption of agentic capabilities across the Life Sciences delivery ecosystem.
- Serve as the voice of delivery by ensuring customer implementation experience directly shapes product evolution, AI investments, and delivery innovation.
- Foster strong partnerships across Product, Engineering, Customer Success, Professional Services, and Global Delivery to deliver measurable customer outcomes.
- Act as a multiplier by identifying patterns, scaling best practices, and enabling reusable solutions that benefit customers globally.
- Build and develop high-performing leaders while fostering a culture of innovation, collaboration, and continuous improvement.
Success in this role will be measured by:
- Adoption of Life Sciences-specific AI assets, accelerators, and reusable delivery capabilities.
- Measurable improvements in implementation quality, delivery productivity, and customer outcomes.
- Product roadmap enhancements influenced by customer implementation feedback.
- Reduction in recurring delivery challenges through reusable assets and standardized delivery patterns.
- Increased AI adoption across Life Sciences customer engagements.
- Growth in reusable implementation assets and industry accelerators leveraged globally.
- Employee engagement, organizational capability, and innovation within the Life Sciences Acceleration Hub.
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.

The typical base salary range for this position is $191,100 - $320,600 annually

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 $228,600 - $348,600 per year. Your recruiter can share more about the specific salary range for the job location during the hiring process.

The range represents base salary only, and does not include company bonus, incentive for sales roles, equity or benefits, as applicable.
Salary Context
This $191K-$348K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 2094 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 10,827 AI roles we're tracking, AI/ML Engineer positions make up 77% 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 $212,160 based on 13,809 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($269K) sits 27% above the category median. Disclosed range: $191K to $348K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,111. 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: $272,300; VP: $236,450.
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, New York, NY, US, New York, NY. Compensation range: $217K - $401K.
Location Context
AI roles in New York pay a median of $221,000 across 3,262 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 10,827 open positions tracked in our dataset. By seniority: 122 entry-level, 5,256 mid-level, 3,922 senior, and 1,527 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (665 positions). The remaining 10,097 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,111. 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 10,827 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,288), AI Software Engineer (548), AI Product Manager (471). 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 (122) are outnumbered by mid-level (5,256) and senior (3,922) 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 1,527 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (665 positions), with 10,097 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 $264,111, 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 (2,093 postings), Aws (1,100 postings), Azure (910 postings), Rag (863 postings), Gcp (680 postings), Pytorch (549 postings), Claude (532 postings), Kubernetes (511 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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