Interested in this AI/ML Engineer role at LPL Financial?
Apply Now →Skills & Technologies
About This Role
Where Ambition Meets Innovation
Build a career that matches all your initiative with an impressive dose of innovation. From cutting-edge resources and a collaborative environment to the freedom to make an impact and more, you’ll find the ingredients you need at LPL Financial to shape your success while helping clients pursue their financial goals.
Job Summary
LPL Financial is seeking a highly technical and hands-on AI engineering leader to help design, build, and deploy next-generation AI solutions for our advisors and business partners.
This role is primarily an individual contributor position responsible for developing production-grade AI and machine learning capabilities while partnering closely with product, engineering, data, and business teams. The ideal candidate combines deep technical expertise in AI/ML and Generative AI with the ability to influence architecture, mentor engineers, and drive adoption of AI solutions across the enterprise.
This position offers the opportunity to shape LPL's AI platform strategy, build advisor-facing AI capabilities, and help establish engineering best practices for scalable, secure, and responsible AI. Over time, this role may provide leadership opportunities for a small team of AI engineers.
Responsibilities:
- Design, develop, and deploy production-grade AI/ML solutions that support LPL Financial's business objectives and advisor experience.
- Architect and build AI-powered applications using modern technologies such as LLMs, generative AI, agentic workflows, RAG architectures, and machine learning models.
- Lead the end-to-end software engineering lifecycle for AI solutions, including design, development, testing, deployment, monitoring, and optimization.
- Partner with Wealth Management, Operations, Risk, Marketing, Product, and Engineering teams to identify high-value AI use cases and translate business requirements into scalable technical solutions.
- Develop and deliver AI-enabled products, platforms, and tools that improve advisor productivity, operational efficiency, and client experience.
- Build and maintain cloud-native AI solutions utilizing AWS services, including Bedrock and related AI/ML technologies.
- Design and implement APIs, microservices, and platform services that enable reusable and scalable AI capabilities across the enterprise.
- Establish engineering best practices for AI development, model evaluation, deployment, observability, security, and responsible AI.
- Collaborate with data, engineering, and architecture teams to ensure AI solutions are secure, compliant, performant, and aligned with enterprise standards.
- Evaluate emerging AI technologies, frameworks, and tooling and recommend opportunities for adoption within LPL.
- Provide technical leadership, mentoring, and architectural guidance to engineers and contribute to the growth of a future AI engineering team.
- Present technical solutions, architecture decisions, and AI innovation opportunities to business and technology stakeholders.
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:
- Minimum of 8 years of software engineering, AI/ML engineering, or machine learning development experience, with a proven track record of building and deploying production AI solutions in complex enterprise environments.
- Strong hands-on programming expertise in Python and experience with modern AI/ML frameworks such as TensorFlow, PyTorch, scikit-learn, LangChain, or similar technologies.
- Experience designing, developing, and deploying Generative AI and machine learning solutions, including LLM-based applications, NLP, RAG architectures, AI agents, model serving, or related AI technologies.
- Strong cloud engineering experience with AWS, Azure, or GCP, including experience deploying scalable AI/ML workloads and cloud-native applications.
- Experience working in highly regulated industries such as Wealth Management, Financial Services, Banking, FinTech, Insurance, Healthcare, or similar environments with strong governance, security, risk, and compliance requirements.
Core Competencies:
- Deep understanding of machine learning concepts including supervised learning, unsupervised learning, deep learning, NLP, recommendation systems, and predictive analytics.
- Experience delivering AI solutions from concept through production deployment, monitoring, and optimization.
- Strong software engineering fundamentals, including APIs, microservices, scalable architectures, testing, and CI/CD practices.
- Experience partnering with product, engineering, and business stakeholders to translate business requirements into technical solutions.
- Knowledge of responsible AI principles, model governance, security, privacy, and risk management.
- Strong communication skills with the ability to explain technical concepts to both technical and non-technical audiences.
- Demonstrated technical leadership, mentoring, and influence across cross-functional teams.
- Strong problem-solving, architectural design, and decision-making skills.
Preferences:
- Experience building advisor-facing, customer-facing, or employee-facing AI products.
- Financial Services, Wealth Management, Brokerage, Asset Management, or FinTech experience.
- Experience with AWS AI services such as Bedrock, SageMaker, Textract, Comprehend, or related AI platforms.
- Experience with vector databases, embeddings, RAG, knowledge graphs, agentic AI, and LLM orchestration frameworks.
- Experience with MLOps, model lifecycle management, observability, and monitoring.
- Experience with big data technologies such as Spark, Hadoop, Databricks, or large-scale data platforms.
- Experience with AI governance, model validation, and regulatory compliance programs.
- Master's degree in Computer Science, Engineering, Data Science, Statistics, or a related quantitative field.
- Bachelor's or Master's degree in Computer Science, Engineering, Data Science, Statistics, or a related quantitative field. Ph.D. preferred. Pay Range:
$133,600.00 - $222,600.00 ###
Actual 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 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.
EAC 5.19.26
Salary Context
This $133K-$222K range is above the median 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 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($178K) sits 16% below the category median. Disclosed range: $133K to $222K.
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.
LPL Financial AI Hiring
LPL Financial has 7 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Fort Mill, SC, Fort Mill, SC, US, New York, NY. Compensation range: $222K - $287K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.