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Overview
Pharmaceutical Research and Manufacturers of America (PhRMA) is seeking a Deputy Vice President, Artificial Intelligence for the Law team to support its legal and policy efforts related to emerging technologies like artificial intelligence (AI) and related issues of importance to the biopharmaceutical industry.
The Deputy Vice President will serve as PhRMA’s lead legal strategist on issues involving data governance and privacy; national and biosecurity; AI-related safety and product liability; AI governance and regulatory oversight and transparency; and AI infrastructure and competitive access impacting the biopharmaceutical industry. This role will provide strategic legal counsel across the organization, lead cross-functional initiatives, engage with member companies and external stakeholders, and help shape PhRMA’s positions on rapidly evolving legal and policy issues at the intersection of technology, innovation, and healthcare.
The position requires a forward-thinking legal leader who can anticipate emerging risks and opportunities, navigate complex regulatory frameworks, and help advance industry priorities in an increasingly digital environment. The Deputy Vice President will:* Provide strategic legal counsel to PhRMA leadership and staff on AI, data governance and privacy, biosecurity, and technology-related issues affecting the association and its members, in coordination with internal subject matter experts and issue area leads.
- Lead the development and implementation of PhRMA’s legal strategy related to artificial intelligence, data privacy, biosecurity, data governance, and emerging technologies of interest to the biopharmaceutical industry.
- Monitor and analyze legislative, regulatory, litigation and policy developments related to AI, data governance and privacy, biosecurity, and related legislative and regulatory developments at the federal, state, and international levels, in coordination with internal subject matter experts and issue area leads.
- Assess emerging legislation, regulatory proposals, liability frameworks, and data governance issues affecting the biopharmaceutical industry and provide actionable recommendations to leadership, in coordination with internal subject matter experts and issue area leads.
- Develop and maintain expertise on emerging legal and regulatory frameworks on AI, including AI governance requirements, privacy and data governance laws, biosecurity considerations, and digital health regulations addressing the use of AI.
- Represent PhRMA before government agencies, policymakers, coalitions, trade associations, and other stakeholders on legal matters related to AI, data governance and privacy, biosecurity, and other emerging technology issues.
- Lead and facilitate member company legal workgroup(s) (as deemed necessary) focused on AI governance and regulations, data governance and privacy, and other emerging technology and biosecurity issues.
- Coordinate with PhRMA colleagues, departments, member companies, outside counsel, consultants, and industry partners to develop and advance policy positions and advocacy strategies.
- Draft legal analyses, position papers, comment letters, guidance documents, presentations, and other materials for internal and external audiences.
- Collaborate with PhRMA attorneys and IT staff on contracts, AI governance, and operational issues involving technology vendors, data-sharing arrangements, cybersecurity matters, and AI-enabled tools, as requested.
- Collaborate with PhRMA attorneys on related legal issues, including FDA regulatory, intellectual property, and international, as requested.
- Build and maintain strong relationships with member company legal, privacy, national security, and government affairs professionals.
- Support crisis response efforts related to biosecurity, data governance and privacy, and other emerging technology issues as needed.
- Perform other duties as assigned. Key Success Factors
PhRMA seeks a visionary, relationship-driven, and impact-focused individual who is passionate about contributing to the health care challenges we face today. The Deputy Vice President leverages deep expertise to guide strategic priorities, anticipates challenges, and aligns departmental efforts with broader organizational goals. A successful DVP is a trusted advisor, skilled in stakeholder influence and resource management, and leads cross-functional initiatives with clarity, confidence, and purpose. Professional Experience* Juris Doctor (J.D.) from an accredited law school.
- Active membership in good standing with at least one state bar.
- Minimum of 10 years of relevant legal experience in privacy, biosecurity, artificial intelligence, technology law, digital health, data governance, regulatory law, or related fields consistent with PhRMA's Deputy Vice President level expectations.
- Demonstrated expertise in federal data governance and privacy laws, emerging AI governance and regulatory frameworks, and other related technology and national security laws/biosecurity programs.
- Significant experience advising organizations on complex legal, regulatory, and policy matters involving technology and data.
- Experience engaging with government agencies, policymakers, trade associations, industry coalitions, or other external stakeholders.
- Strong understanding of the legal and policy implications of artificial intelligence, machine learning, automated decision-making systems, and other emerging technologies.
- Exceptional writing, editing, analytical, and presentation skills.
- Proven ability to synthesize complex legal and technical concepts for executive and non-technical audiences.
- Demonstrated experience leading strategic initiatives and managing multiple high-priority projects simultaneously.
- Experience developing and maintaining productive relationships with senior stakeholders.
- Ability to exercise sound judgment and manage sensitive and confidential matters.
- Strong project management and organizational skills.
- Must be willing to travel as requested and be responsive/available after hours and on weekends.
Preferred Experience* Experience within the biopharmaceutical, healthcare, life sciences, technology, or highly regulated sectors.
- Experience leading industry workgroups, coalitions, or public policy initiatives.
- Certified Information Privacy Professional (CIPP), Certified Information Privacy Manager (CIPM), or similar credentials.
- Experience managing outside counsel and consultants. Potential Salary
$243,200-$346,700 per annum. Salary is commensurate with experience and other compensable factors. Who we are
The Pharmaceutical Research and Manufacturers of America (PhRMA) represents the country’s leading innovative biopharmaceutical research companies, which are laser focused on developing innovative medicines that transform lives and create a healthier world. Together, we are fighting for solutions to ensure patients can access and afford medicines that prevent, treat and cure disease. PhRMA member companies have invested more than $900 billion in the search for new treatments and cures over the last decade, supporting nearly five million jobs in the United States. Connect with PhRMA
For information on how innovative medicines save lives, please visit:
www.PhRMA.org www.Innovation.org www.AmericasMedicines.org www.Facebook.com/PhRMA www.Twitter.com/PhRMA What we offer
In addition to a highly competitive salary and bonus program, various opportunities for reward and recognition and a platform of extensive benefits, PhRMA is committed to the development and overall wellbeing of our team members. We offer traditional (medical, dental, vision, flexible spending, life, AD&D, LTD, STD) and enhanced benefits such as parental leave, a well-being program, an on-site fitness facility, back-up care, health advocate service, an employee assistance program, and commuting benefits. We also offer a robust 401k plan with employer contributions upon the first day of hire and immediate vesting, a generous paid time off plan, ten paid holidays (plus inauguration day), half day Fridays preceding holidays, half day Fridays in the summer months, and a paid winter break. As an organization, we work together in the office on Mondays through Thursdays and remotely on Fridays. We also all work remotely in August.
We are committed to the growth and development of our team members and offer many learning opportunities including an integrated onboarding program, best-in-class leadership programming, tuition reimbursement, industry on-site and off-site training, and other management/professional development programs. Corporate social responsibility is also important to us at PhRMA. Our team members participate in community service activities, fundraising drives and charitable athletic events.
We are committed to supporting our family of professionals at PhRMA and strive to create programs that help our team members manage the challenges of balancing a fast-paced career with their personal goals. Equal Opportunity Employer PhRMA provides equal employment opportunities to all applicants without regard to race, color, religion, national origin, sex, age, marital status, personal appearance, sexual orientation, gender identity or expression, family responsibilities, genetic information, disability, matriculation, political affiliation or veteran status in accordance with applicable federal, state and local laws governing nondiscrimination in employment. The job description contains an overview of the activities and duties for this role. Responsibilities may change and new ones may be assigned at any time.
Salary Context
This $243K-$346K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At PHRMA, 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 in Demand for This Role
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. This role's midpoint ($294K) sits 39% above the category median. Disclosed range: $243K to $346K.
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.
PHRMA AI Hiring
PHRMA has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Washington, DC, US. Compensation range: $346K - $346K.
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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