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About This Role
The Associate Director leads a small team of software engineers responsible for designing and delivering the data pipelines, retrieval systems, and knowledge integrations that power a generative AI platform. This role combines technical leadership with people management — setting architectural direction, removing blockers, and ensuring the team delivers high\-quality, scalable solutions. The Associate Director balances hands\-on technical involvement with strategic planning, stakeholder communication, and team development.Essential Job Functions:
- Directly manage team(s) focused on delivering high quality project results within one or more major technology disciplines: strategy, software development, operations, engineering, development services, information security, and compliance.
- Focus on team leadership, delivery of business value, risk mitigation, cost optimization and return on investment.
- Deliver results based upon FINRA annual goals, department goals and management requests.
- Direct and coordinate organization's financial and budget activities to fund operations, maximize investments, and increase efficiency for a project(s).
- Ensure technology best practices are being implemented and improved within team(s).
- Analyze information and evaluate results to choose the best solutions and solve problems.
- Support the evaluation of new technologies, techniques, and tools.
- Report status and issues to senior Technology management team.
- Work directly with outside vendors to negotiate services and product agreements
- Willingness to lead, take responsibility and offer options.
- Serve as a backup to more senior level management as needed.
- Develop constructive and cooperative working relationships with peers both within and outside of Technology, and maintain them over time.
- Establish and maintain external relationships with strategic product/service vendors and industry forums (government \& private councils, standards bodies, product forums, etc.), as appropriate.
- Contribute to the establishment, evolution and continued compliance with standard practices and processes within the disciplines.
- Assist with adherence to technology policies and comply with all security controls.
- Ensure all work products meets/exceeds FINRA standards and risks are effectively managed.
- Participate in periodic Disaster Recovery (DR), Business Continuity Planning (BCP) and Sarbanes Oxley (SOX) testing and reporting.
Resource Management:
- Identify and hire resource/skills needed within their organization.
- Responsible for staff performance management and training.
- Coordinate assignment of subordinate staff.
- Demonstration of FINRA’s values.
- Collaboration, both in\-person and virtually, in furtherance of FINRA’s mission of investor protection and market integrity.
Key Responsibilities:
Technical Leadership \& Architecture
- Set the technical vision and architectural direction for knowledge framework integrations, including data ingestion, embedding pipelines, and retrieval systems
- Make key design decisions on pipeline architecture, data modeling, and search strategies, balancing quality, performance, and delivery timelines
- Evaluate and recommend technologies, frameworks, and approaches for knowledge integration challenges
- Maintain sufficient hands\-on involvement to provide credible technical guidance and unblock the team on complex problems
- Demonstrate solid understanding of systems and processes within the domain and recommend improvements
Team Leadership \& People Management
- Manage, mentor, and develop a small team of software engineers, fostering a culture of ownership, collaboration, and continuous improvement
- Conduct regular one\-on\-ones, provide constructive feedback, and support career growth for direct reports
- Plan and prioritize team workload, balancing feature delivery, technical debt, and operational responsibilities
- Recruit, onboard, and ramp new team members as the team scales
- Champion collaborative resolution of defects and lead internal process improvement initiatives
Engineering Excellence \& Quality
- Establish and enforce engineering standards for code quality, testing, documentation, and peer review across the team
- Guide the team in best practices for data pipeline development, retrieval system design, and test automation
- Ensure comprehensive test coverage including unit testing, integration testing, and retrieval quality evaluation
- Drive adoption of test\-driven development (TDD) practices and automated quality gates
Planning \& Delivery
- Own the team's roadmap and delivery commitments, translating business requirements into actionable technical plans
- Break down complex integration initiatives into well\-scoped work items with clear acceptance criteria
- Track progress, identify risks early, and adjust plans to meet deadlines without sacrificing quality
- Provide regular status updates and delivery forecasts to leadership
Stakeholder Collaboration
- Partner with product, platform engineering, and agent development teams to align knowledge integration priorities with broader platform goals
- Communicate complex or technical information at the appropriate level for diverse audiences including leadership, peers, and external partners
- Represent the team in cross\-functional planning sessions, architecture reviews, and prioritization discussions
- Build strong working relationships across the organization to facilitate collaboration and shared accountability
Generative AI \& Emerging Technologies
- Stay informed of advances in RAG architectures, embedding models, retrieval optimization, and knowledge management approaches
- Evaluate emerging technologies and techniques for applicability to the team's work, making build\-vs\-buy recommendations
- Drive innovation within the team by creating space for experimentation and proof\-of\-concept work
Security \& Compliance
- Ensure all team deliverables meet or exceed organizational standards for secure development practices
- Oversee secure handling of sensitive domain data throughout the ingestion and retrieval lifecycle
- Assist with adherence to technology policies and ensure the team complies with all security controls
- Participate in threat modeling and security discussions for data pipeline and infrastructure components
Education/Experience Requirements:
- Bachelor’s degree in Computer Science, Information Systems or related discipline with at least five (5\) years of related experience, or equivalent training and/or work experience; Master’s degree and past Financial Services industry experience preferred.
- Minimum of 3 years of managing technical programs and/or processes including staff management of at least four (4\) resources and budget of at least $1 million annually.
- Knowledge of business, technology and management principles involved in strategic planning, organizational change management, resource allocation, human resources modeling, leadership technique, and coordination of people and resources.
- Experience must include direct project experience in one of the following areas: strategy, software development, operations, engineering, development services, information security, and/or compliance.
- Extensive knowledge of industry leading technology best practices including familiarity with technology methodologies including at least one of ISO\-9000, ITIL, Agile and iterative.
- Excellent written and verbal technical communication skills.
- Demonstrated ability to develop effective working relationships and leverage those relationships to improve the quality of work products.
- Must be able to identify, evaluate and recommend processes, tools, technologies and/or products to meet business and budgetary requirements.
- Should be well organized, thorough, and able to handle competing priorities.
- Ability to maintain focus and develop proficiency in new skills rapidly.
- Ability to work in a fast paced environment.
Work Conditions:
- Hybrid work environment, with defined in\-person presence requirements.
- Occasional travel and extended hours may be required.
For work that is performed in CO, FL, TX, IL, PA, MA, MD, VA, Washington, DC, NY and NJ, please refer to the chart below for the salary range for the corresponding location. FINRA complies with all state and local pay transparency laws and regulations requiring the disclosure of salary ranges for the position. In addition to location, actual compensation is based on various factors, including but not limited to, the candidate’s skill set, level of experience, education, and market considerations.
CO/FL/TX: Minimum Salary $114,200, Maximum Salary $207,200
IL/PA: Minimum Salary $125,900, Maximum Salary $228,000
MA/MD/VA/Washington, DC: Minimum Salary $131,200, Maximum Salary $238,300
NY/NJ: Minimum Salary $131,200, Maximum Salary $248,700
\#LI\-Hybrid
To be considered for this position, please submit an application. Applications are accepted on an ongoing basis.
*The information provided above has been designed to indicate the general nature and level of work of the position. It is not a comprehensive inventory of all duties, responsibilities and qualifications required.*
*Please note: If the “Apply Now” button on a job board posting does not take you directly to the FINRA Careers site, enter www.finra.org/careers into your browser to reach our site directly.*
Employees may be eligible for a discretionary bonus in addition to base pay. Non\-exempt employees are also eligible for overtime pay in accordance with federal, state, or local law. As part of its dedication to employee wellness, FINRA provides comprehensive health, dental and vision insurance. Additional insurance includes basic life, accidental death and dismemberment, supplemental life, spouse/domestic partner and dependent life, and spouse/domestic partner and dependent accidental death and dismemberment, short\- and long\-term disability, long\-term care, business travel accident, disability and legal. FINRA offers immediate participation and vesting in a 401(k) plan with company match and eligibility for participation in an additional FINRA\-funded retirement contribution, tuition reimbursement, commuter benefits, and other benefits that support employee wellness, such as adoption assistance, backup family care, surrogacy benefits, employee assistance, and wellness programs.
Time Off and Paid Leave\*
FINRA encourages its employees to focus on their health and wellness in many ways, including through a generous time\-off program of 15 days of paid time off, 5 personal days and 9 sick days, unless otherwise required by law (all pro\-rated in the first year). Additionally, we are proud to support our communities by providing two volunteer service days (based on full\-time schedule). Other paid leave includes military leave, jury duty leave, bereavement leave, voting and election official leave for federal, state or local primary and general elections, care of a family member leave (available after 90 days of employment); and childbirth and parental leave (available after 90 days of employment). Full\-time employees receive nine paid holidays.
- Based on full\-time schedule
Important Information
FINRA’s Code of Conduct imposes restrictions on employees’ investments and requires financial disclosures that are uniquely related to our role as a securities regulator. FINRA employees are required to disclose to FINRA all brokerage accounts that they maintain, and those in which they control trading or have a financial interest (including any trust account of which they are a trustee or beneficiary and all accounts of a spouse, domestic partner or minor child who lives with the employee) and to authorize their broker\-dealers to provide FINRA with duplicate statements for all of those accounts. All of those accounts are subject to the Code’s investment and securities account restrictions, and new employees must comply with those investment restrictions—including disposing of any security issued by a company on FINRA’s Prohibited Company List or obtaining a written waiver from their Executive Vice President—by the date they begin employment with FINRA. Employees may only maintain securities accounts that must be disclosed to FINRA at one or more securities firms that provide an electronic feed (e\-feed) of data to FINRA, and must move securities accounts from other securities firms to a firm that provides an e\-feed within three months of beginning employment.
You can read more about these restrictions here.
As standard practice, employees must also execute FINRA’s Employee Confidentiality and Invention Assignment Agreement without qualification or modification and comply with the company’s policy on nepotism.
Search Firm Representatives
Please be advised that FINRA is not seeking assistance or accepting unsolicited resumes from search firms for this employment opportunity. Regardless of past practice, a valid written agreement and task order must be in place before any resumes are submitted to FINRA. All resumes submitted by search firms to any employee at FINRA without a valid written agreement and task order in place will be deemed the sole property of FINRA and no fee will be paid in the event that person is hired by FINRA.
FINRA is an Equal Opportunity Employer
All qualified applicants receive consideration for employment without regard to any legally protected category, including race, color, age, national origin, ethnicity, religion, disability, genetic information, military or veteran status, sex, or any other status or classification protected by state or local law.
FINRA strives to make our career site accessible to all users. If you need a disability\-related accommodation for completing the application process, please contact FINRA’s Employee Relations team at 240\-386\-4865 or by email at [email protected]. Please note that this process is exclusively for inquiries regarding accommodations in the application process.
FINRA abides by the requirements of 41 CFR 60\-741\.5(a). This regulation prohibits discrimination against qualified individuals on the basis of disability and requires affirmative action by covered prime contractors and subcontractors to employ and advance in employment qualified individuals with disabilities.
FINRA abides by the requirements of 41 CFR 60\-300\.5(a). This regulation prohibits discrimination against qualified protected veterans and requires affirmative action by covered prime contractors and subcontractors to employ and advance in employment qualified protected veterans.
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Salary Context
This $114K-$248K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At FINRA, 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($181K) sits 16% below the category median. Disclosed range: $114K to $248K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
FINRA AI Hiring
FINRA has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Rockville, MD, US. Compensation range: $248K - $248K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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