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About This Role
Great benefits. Competitive pay. We know these are some of the things people look for in a job.
If you’re the kind of person who believes that honoring and empowering our nation’s veterans is more than just a cause — that it’s a calling — then we’re ready to meet you.
When you join Wounded Warrior Project® (WWP), you’re committing to making a difference. We make a commitment to you, too – helping you to find that spark, ignite your passion to serve, and embark on a career with meaning and purpose.
At WWP™, we recognize our mission cannot be accomplished without our talented teammates, which is why we’re proud to offer benefits such as:
- A flexible hybrid work schedule (three days in the office, two days’ work from home)
- Full medical, dental, and vision coverage for both teammates AND family members
- Competitive pay and performance incentives
- A fun, mission-focused, and collaborative team environment
A mission that matters is just the beginning, so if you’re ready to get started, we’re ready for you.
The Wounded Warrior Project (WWP) Salesforce AI Engineer is responsible for designing, building, implementing, and optimizing artificial intelligence solutions within the Salesforce ecosystem. This role leverages Salesforce Einstein, Agentforce, Data Cloud, CRM Analytics, and generative AI capabilities to improve engagement, service operations, business processes, and teammate productivity. The Salesforce AI Engineer partners with business stakeholders, Salesforce administrators, developers, data teams, and organizational leaders to identify AI opportunities, deliver scalable and secure solutions, and promote responsible AI governance, adoption, and innovation across the organization. DUTIES & RESPONSIBILITIES
- Identify opportunities to leverage Salesforce AI capabilities across Service Cloud, Marketing Cloud, Experience Cloud, and other Salesforce platform technologies to improve customer experience, operational efficiency, teammate productivity, and business outcomes.
- Design, build, implement, and maintain AI solutions using Salesforce Einstein, Agentforce, Data Cloud, CRM Analytics, Flow, Apex, and related Salesforce technologies.
- Configure and support Agentforce agents, conversational AI experiences, and intelligent automation solutions that enhance customer and teammate interactions.
- Develop and optimize prompt templates, AI workflows, and generative AI solutions in alignment with organizational governance and security standards.
- Configure and maintain Data Cloud capabilities, including data models, customer profiles, identity resolution, and governance controls that support AI-driven outcomes.
- Integrate Salesforce AI solutions with enterprise applications, APIs, data platforms, and third-party technologies.
- Evaluate emerging Salesforce AI and generative AI technologies, conduct proof-of-concept activities, and recommend innovative solutions that support business objectives.
- Establish and support AI governance, security, privacy, compliance, and responsible AI practices.
- Lead solution design discussions and provide technical expertise on Salesforce AI architecture, implementation approaches, and best practices.
- Troubleshoot and resolve issues related to AI solutions, automations, integrations, data quality, and platform performance.
- Develop and maintain technical documentation, implementation standards, operational procedures, and training materials.
- Collaborate with business stakeholders, Salesforce administrators, developers, data teams, security teams, and leadership to deliver scalable, secure, and effective AI solutions.
- Monitor AI solution performance, user adoption, and business outcomes, identifying opportunities for continuous improvement and optimization.
- Attend and actively participate in required training and/or meetings, including but not limited to New Teammate Orientation, WWP Cares, ASIST Suicide Prevention training, Leadership training, culture/team based training, or departmental huddles.
- Other related duties as assigned.
KNOWLEDGE, SKILLS, & ABILITIES
- Strong working knowledge of Salesforce platform architecture, AI capabilities, automation tools, reporting solutions, integrations, and data management concepts.
- Strong understanding of artificial intelligence and machine learning concepts, including generative AI, large language models (LLMs), prompt engineering, predictive analytics, and natural language processing.
- Demonstrated ability to translate business requirements into scalable AI-enabled Salesforce solutions that improve operational efficiency, customer experiences, and business outcomes.
- Demonstrated ability to design, configure, integrate, and support Salesforce-based solutions.
- Demonstrated working knowledge of Salesforce development, automation, APIs, and integration technologies.
- Strong understanding of AI governance, data privacy, security, and responsible AI practices.
- Familiarity with Agile methodologies, software development lifecycle (SDLC) practices, and DevOps concepts.
- Strong analytical, problem-solving, and critical-thinking skills with the ability to troubleshoot technical issues and identify effective solutions.
- Excellent written, verbal, and presentation skills with the ability to communicate effectively with technical and non-technical stakeholders.
- Strong organizational skills with the ability to manage multiple priorities, projects, and deadlines.
- Ability to work independently and collaboratively within cross-functional teams.
- Unequivocal commitment to the highest standards of personal and business ethics and conduct.
- Mission-driven, guided by core values, and a pleasure to work with.
EXPERIENCE
Requirements
- Four years of experience designing, configuring, developing, or supporting solutions within the Salesforce platform.
- Two years of experience working with artificial intelligence, machine learning, predictive analytics, generative AI, or related technologies.
- Two years of experience building AI-enabled, agent-based, automated, or multi-step workflows and translating business requirements into technical solutions.
Preferences
- Experience implementing enterprise AI solutions within Salesforce, including Service Cloud, Marketing Cloud, or other customer engagement environments.
- Experience working with Azure OpenAI, Microsoft Copilot, AWS AI services, or similar generative AI platforms.
- Experience working in Agile or Scrum environments and applying AI governance, responsible AI, privacy, and data management practices.
EDUCATION
Requirements
- Bachelor’s degree in computer science, information systems, or related field.
Preferences
- None.
CERTIFICATIONS & LICENSURE
Requirements
- Salesforce Certified Agentforce Specialist.
- Salesforce Certified Administrator.
Preferences
- Relevant Salesforce certifications, such as Salesforce Certified Agentforce Specialist, Salesforce Certified Platform App Builder, Salesforce Certified Data Cloud Consultant, Salesforce Certified Platform Developer I, or similar credentials.
WORK ENVIRONMENT/PHYSICAL DEMANDS
- General office environment; temperature controlled.
- Sitting for extended periods.
- Up to 5% travel.
- ca-pd
#LI-HYBRID
Wounded Warrior Project® is an equal opportunity employer committed to providing equal employment opportunity to all persons without regard to race, color, religion, national origin, gender, gender identity, sexual orientation, marital status, citizenship, age, veteran or military status, disability, genetic information, or any other characteristic protected by law.
Please note:
Wounded Warrior Project is not seeking assistance or accepting unsolicited resumes from search firms without a written search agreement in place. All resumes submitted by search firms to any employee at Wounded Warrior Project via email, the Internet or directly to hiring managers at Wounded Warrior Project in any form without a valid written search agreement in place will be deemed the sole property of Wounded Warrior Project, and no fee will be paid in the event the candidate is hired by Wounded Warrior Project as a result of the referral or through other means."
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Wounded Warrior Project, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Wounded Warrior Project AI Hiring
Wounded Warrior Project has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Jacksonville, FL, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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