Interested in this AI/ML Engineer role at Veeva Systems?
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About This Role
Veeva is building the industry cloud for Life Sciences through software, data, AI, and business consulting working together. Learn more about our products, vision, values, and status as a Public Benefit Corporation (PBC) on our website.
Consulting is changing, and we’re not like other firms. Launched in 2019, Veeva Business Consulting has grown quickly and organically to over 500 global experts who combine deep domain knowledge with innovative technology to help Life Sciences companies work in a more efficient and connected way.
We are growing aggressively as we look towards 2030. We are seeking great people to help shape the future of the industry with the world’s leading software, data, AI, and expertise at their back.
The Role Veeva is seeking an AI Solution Analyst to join our Business Consulting team. In this role, you will sit at the intersection of consulting, AI enablement, business process, and technology to maximize the value of enterprise AI platforms (such as Claude and Gemini). You will work directly with consultants and business leaders to identify high-impact workflow opportunities, prototype AI solutions, drive enablement, and scale repeatable AI capabilities across our global consulting organization. What You'll Do Serve as the hands-on subject matter expert for enterprise AI platforms, guiding teams on optimal platform selection, capabilities, and practical use cases Partner with consulting teams to discover, evaluate, and prioritize AI and automation opportunities across business workflows Rapidly prototype, test, and validate AI solution concepts, standardizing high-value prompts, workflows, and reusable assets Partner with AI Engineering to transition complex, validated concepts into scalable enterprise applications and integrations Lead AI enablement initiatives, including workshops, hands-on training, prompt engineering guidance, and change management Establish quality frameworks, test cases, and evaluation criteria to ensure AI output accuracy, reliability, and risk mitigation Track, measure, and report on AI adoption, productivity gains, and business impact across consulting teams Reinforce AI governance, data privacy, and security guidelines across all proposed solutions Requirements Bachelor's degree in Computer Science, Business, Information Systems, or a related field 3+ years of experience in business analysis, technology consulting, AI enablement, or solution analysis Strong hands-on experience with generative AI platforms (such as Claude and Gemini) and advanced AI solutions Proven ability to analyze business processes and translate complex business problems into structured AI use cases Experience evaluating AI output quality, designing test scenarios, and identifying risk or hallucination edge cases Excellent communication and facilitation skills, with experience conducting technical workshops, office hours, or training Continuous enthusiasm for emerging technology with strong logical thinking and problem-solving abilities Nice to Have Experience in management consulting, life sciences, or enterprise SaaS domains Familiarity with enterprise AI governance, security, and responsible AI practices Understanding of enterprise integration concepts, APIs, and automation tooling Experience defining productivity metrics, baselines, and measuring technology ROI Interviewing with Veeva
We value your time and believe in a transparent hiring process. Here is the process you can expect.
Follow the application process and submit your resume. Within 3 days, you will receive a link to a personality assessment administered by a third party. Once you complete the assessment, our team will review your full application package and follow up via email with our decision. If moving to the interview stage, the process is as follows: A conversation with the hiring manager A practical case exercise A final conversation with our group's Senior Leader. Once all interviews are complete, the manager will be in touch with a final decision. Perks & Benefits Medical, dental, vision, and basic life insurance Flexible PTO and company paid holidays Retirement programs 1% charitable giving program Unrivaled career progression in a fast-growing, global team Veeva is a Work Anywhere company. You can work from home or the office on any given day Compensation Base pay: $80,000 - $200,000 The salary range listed here has been provided to comply with local regulations and represents a potential base salary range for this role. Please note that actual salaries may vary within the range above or below, depending on experience and location. We look at compensation for each individual and base our offer on your unique qualifications, experience, and expected contributions. This position may also be eligible for other types of compensation in addition to base salary, such as variable bonus and/or stock bonus. #LI-RemoteUS #LI-MidSenior
Veeva’s headquarters is located in the San Francisco Bay Area with offices in more than 15 countries around the world.
Veeva is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, sex, sexual orientation, gender identity or expression, religion, national origin or ancestry, age, disability, marital status, pregnancy, protected veteran status, protected genetic information, political affiliation, or any other characteristics protected by local laws, regulations, or ordinances. If you need assistance or accommodation due to a disability or special need when applying for a role or in our recruitment process, please contact us at [email protected].
Salary Context
This $80K-$200K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Veeva Systems, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($140K) sits 34% below the category median. Disclosed range: $80K to $200K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Veeva Systems AI Hiring
Veeva Systems has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Agent Developer. Positions span Remote, US, Chengdu, Sichuan, China. Compensation range: $200K - $200K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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