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About This Role
What if you could build a career where ambition meets innovation? At LPL Financial, we empower professionals to shape their success while helping clients pursue their financial goals with confidence. What if you could have access to cutting-edge resources, a collaborative environment, and the freedom to make an impact? If you're ready to take the next step, discover what’s possible with LPL Financial.
Job Overview:
LPL is seeking a highly skilled and experienced Vice President, Assistant General Counsel- AI to join our Legal Privacy team and serve as an AI Champion. This role will advise on the legal, regulatory, and ethical considerations related to artificial intelligence across the enterprise, including the evaluation of third-party AI tools, development of proprietary models, and the governance of data used to train and operate such systems. The attorney will partner closely with the AI team, Product, Cybersecurity, Technology, Risk, and business stakeholders to ensure compliant and responsible AI development and use across the organization. This role combines deep subject-matter expertise in AI, privacy, financial services regulations, and technology with the ability to execute in a fast-paced environment, manage complex matters under tight deadlines, and provide practical, business-oriented advice. The attorney will also support contract negotiation and drafting, including AI-specific terms, and play a critical role in strengthening the firm’s AI governance program, data protection practices, and risk-management processes.
Responsibilities
- Serve as a primary legal advisor on AI-related matters, including regulatory requirements, risk mitigation, model governance, and data protection.
- Support the evaluation, review, and onboarding of new AI tools, including analysis of data use, model behavior, licensing, and compliance impacts.
- Advise on the design, development, and deployment of proprietary AI models, ensuring alignment with applicable laws, industry standards, and internal governance policies.
- Draft, negotiate, and manage AI-specific contract terms, including terms of use, data-use provisions, and AI addenda for vendor relationships.
- Provide legal guidance on privacy, cybersecurity, technology, and financial services regulatory requirements implicated by AI use cases.
- Partner with AI Governance, Risk, Engineering, Technology, and business teams to develop and enhance policies, controls, and best practices for responsible AI.
- Assess and communicate legal risks associated with AI and data use, providing clear, practical recommendations to business leadership.
- Monitor and interpret emerging AI, privacy, and technology regulations, advising stakeholders on operational impacts and readiness planning.
- Support incident response related to AI or data-related events, coordinating with cybersecurity and privacy teams as needed.
- Contribute to enterprise training, awareness, and change-management efforts related to responsible AI and data governance.
- Advise on legal and regulatory issues related to the use of AI in broker-dealer functions, including trade surveillance, client onboarding, and compliance automation.
- Provide legal support for AI model validation and development, explainability, accuracy and risk assessments.
What are we looking for?
We’re looking for strong collaborators who deliver exceptional client experiences and thrive in fast-paced, team-oriented environments. Our ideal candidates pursue greatness, act with integrity, and are driven to help our clients succeed. We value those who embrace creativity, continuous improvement, and contribute to a culture where we win together and create and share joy in our work.
Requirements
- Juris Doctor (JD) from an accredited law school; licensed to practice in at least one U.S. jurisdiction.
- 8–12+ years of legal experience, with a strong background in:
+ Broker-dealer and registered investment advisory regulatory compliance (SEC, FINRA, MSRB).
+ AI, data privacy, cybersecurity, and technology law.
Core Competencies
- Regulatory Expertise: Strong working knowledge of AI-related laws and guidance (e.g., EU AI Act, U.S. state AI and privacy laws, sector-specific regulations, federal proposals), as well as privacy and cybersecurity frameworks (GLBA, Reg S-P, NIST, FFIEC).
- Technical Curiosity and Fluency: Ability to understand AI concepts (e.g., training data, model architectures, bias/variance, evaluations, prompt injection, data lineage) and translate technical details into legal implications.
- Risk-Based Judgment: Skilled in assessing complex fact patterns and delivering balanced, pragmatic legal advice.
- Contracting and Negotiation: Experience drafting and negotiating technology, data-use, licensing, and AI-specific agreements.
- Cross-Functional Collaboration: Comfort working with technologists, product teams, risk partners, and senior leaders.
- Strategic Thinking: Ability to anticipate regulatory trends and emerging risks and guide sustainable governance practices.
- Communication Excellence: Clear, concise written and verbal communication that enables informed decision-making.
- Execution Under Pressure: Demonstrated ability to meet deadlines, manage competing priorities, and operate effectively in a high-demand environment.
- Ethical and Responsible AI Mindset: Strong orientation toward fairness, transparency, accountability, and safe use of data.
- Ability to translate complex AI and regulatory concepts into actionable legal advice.
- Experience advising on AI model validation, surveillance algorithms, and automated compliance tools.
Preferences
- Experience advising on AI model governance, algorithmic accountability, and data ethics.
- Strong contract negotiation and drafting skills.
- Familiarity with securities laws, data mapping, and product development.
- In-house experience in a financial services organization a plus.
- CIPP/US, CIPP/E, CIPM, or CIPT (IAPP)
- AI Governance or AI Risk Certifications, such as:
+ IAPP AIGP (Artificial Intelligence Governance Professional)
+ ICA AI Governance Certification
+ MIT, Stanford, or Oxford AI governance programs
- Cybersecurity Certifications (if relevant to scope):
+ ISC2 CC, CISSP, or SSCP (awareness-level acceptable)
- Technology/AI Fundamentals Certifications, such as:
+ Microsoft AI-900 (AI Fundamentals)
+ Google Cloud Generative AI Credential
+ IBM Applied AI certificate
Pay Range:
$172,036-$286,726/yearActual base salary varies based on factors, including but not limited to, relevant skill, prior experience, education, base salary of internal peers, demonstrated performance, and geographic location. Additionally, LPL Total Rewards package is highly competitive, designed to support your success at work, at home, and at play – such as 401K matching, health benefits, employee stock options, paid time off, volunteer time off, and more. Your recruiter will be happy to discuss all that LPL has to offer!
Company Overview:
LPL Financial Holdings Inc. (Nasdaq: LPLA) is among the fastest growing wealth management firms in the U.S. As a leader in the financial advisor-mediated marketplace(6) , LPL supports over 32,000 financial advisors and the wealth management practices of approximately 1,100 financial institutions, servicing and custodying approximately $2.3 trillion in brokerage and advisory assets on behalf of approximately 8 million Americans. The firm provides a wide range of advisor affiliation models, investment solutions, fintech tools and practice management services, ensuring that advisors and institutions have the flexibility to choose the business model, services, and technology resources they need to run thriving businesses. For further information about LPL, please visit www.lpl.com.
At LPL, independence means that advisors and institution leaders have the freedom they deserve to choose the business model, services, and technology resources that allow them to run a thriving business. They have the flexibility to do business their way. And they have the freedom to manage their client relationships, because they know their clients best. Simply put, we take care of our advisors and institutions, so they can take care of their clients.
For further information about LPL, please visit www.lpl.com.
Join LPL Financial: Where Your Potential Meets Opportunity
At LPL Financial, we believe that everyone deserves objective financial guidance. As the nation’s leading independent broker-dealer, we offer an integrated platform of cutting-edge technology, brokerage, and investment advisor services.
Why LPL?
- Innovative Environment: We foster creativity and growth, providing a supportive and responsive leadership team. Learn more about our leadership team here!
- Limitless Career Potential: Your career at LPL has no limits, only amazing potential. Learn more about our careers here!
- Unified Mission: We are one team on one mission—taking care of our advisors so they can take care of their clients. Learn more about our mission and values here!
- Impactful Work: Our size is just right for you to make a real impact. !
- Commitment to Equality: We support workplace equality and embrace diverse perspectives and backgrounds. !
- Community Focus: We care for our communities and encourage our employees to do the same. !
- Benefits and Total Rewards: Our Total Rewards package goes beyond just compensation and insurance. It includes a mix of traditional and unique benefits, perks, and resources designed to enhance your life both at work and at home. !
Join the LPL team and help us make a difference by turning life’s aspirations into financial realities. Please log in or create an account to apply to this position. Principals only. EOE.
Information on Interviews:
LPL will only communicate with a job applicant directly from an @lplfinancial.com email address and will never conduct an interview online or in a chatroom forum. During an interview, LPL will not request any form of payment from the applicant, or information regarding an applicant’s bank or credit card. Should you have any questions regarding the application process, please contact LPL’s Human Resources Solutions Center at (855) 575-6947.
EAC12.9.25
Salary Context
This $172K-$286K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $170K across 217 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 37,339 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At LPL Financial, 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 $154,000 based on 8,743 positions with disclosed compensation. This role's midpoint ($229K) sits 49% above the category median. Disclosed range: $172K to $286K.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
LPL Financial AI Hiring
LPL Financial has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Fort Mill, SC, US. Compensation range: $286K - $286K.
Location Context
Across all AI roles, 7% (2,732 positions) offer remote work, while 34,484 require on-site attendance. Top AI hiring metros: New York (1,633 roles, $204,100 median); Los Angeles (1,356 roles, $179,440 median); San Francisco (1,230 roles, $240,000 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 37,339 open positions tracked in our dataset. By seniority: 3,672 entry-level, 23,272 mid-level, 7,048 senior, and 3,347 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (2,732 positions). The remaining 34,484 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 37,339 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (33,926), AI Software Engineer (823), AI Product Manager (805). 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 (3,672) are outnumbered by mid-level (23,272) and senior (7,048) 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 3,347 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (2,732 positions), with 34,484 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $145,600. 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: Rag (23,721 postings), Aws (12,486 postings), Rust (10,785 postings), Python (5,564 postings), Azure (3,616 postings), Gcp (3,032 postings), Prompt Engineering (2,112 postings), Kubernetes (1,713 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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