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Job Description:
At Bank of America, we are guided by a common purpose to help make financial lives better through the power of every connection. We do this by driving Responsible Growth and delivering for our clients, teammates, communities and shareholders every day.
Being a Great Place to Work and providing a culture of caring is core to how we drive Responsible Growth. We are intentional about fostering an inclusive workplace where every teammate has the opportunity to succeed, build a career and contribute to our shared success. This includes attracting and developing exceptional talent, recognizing and rewarding performance, and supporting our teammates’ physical, emotional, and financial wellness through affordable, competitive and flexible benefits.
We value the unique perspectives individuals bring from all backgrounds and career paths \- whether shaped by military service, community college education, or a wide range of work and life experiences. These journeys foster resilience, leadership and innovation, strengthening our workforce and positively impact the communities we serve.
Bank of America is committed to an in\-office culture that supports collaboration, engagement, and career development. Our approach includes clear in\-office expectations, while providing an appropriate level of flexibility based on role\-specific responsibilities and business needs.
At Bank of America, you can build a successful career with opportunities to learn, grow, and make an impact. Join us!
Job Description:
This job is responsible to lead and direct work across the team and develop and deliver the overall marketing strategy for a portfolio of programs/products/processes. Key responsibilities include using data to make strategic marketing program decisions, building partnerships across the enterprise to drive priorities and project direction, making complex decisions delivering notable impact at the division level, and demonstrating return on investment. Job expectations include identifying risks, tracking to budgets/scope of work, defining/prioritizing solutions and deploying required resources.
Content is at the core of Bank of America's business approach, with high regard and demand for content programming across the company. The Content Marketing Center of Excellence supports across lines of business in delivering best\-in\-class content marketing programs. As Bank of America accelerates its transformation through AI, the Content Marketing Center of Excellence is seeking an AI Content Strategy \& Enablement Lead to help define how content strategy, content marketing, and content experiences evolve in an increasingly AI\-powered landscape while also serving as the Content Strategy Lead for the Consumer line of business.
This role requires a strong content strategist with practical AI experience and a deep understanding of how large language models, evolving discovery behaviors, and changing customer expectations are reshaping content marketing. The successful candidate will help modernize content strategy and operations through AI\-enabled approaches, develop scalable frameworks and best practices, and help guide how the organization adapts its content marketing practices for an AI\-driven world. The ideal candidate is a strategic thinker, AI early adopter, and hands\-on operator who can navigate ambiguity, influence stakeholders, and drive execution within a complex, matrixed organization.
AI\-Powered Content Strategy \& Enablement:
- Drive advancements in content strategy, development, and optimization through the thoughtful application of AI and human expertise.
- Define how content strategy should evolve as customers increasingly engage with AI\-powered experiences, conversational interfaces and emerging forms of content discovery.
- Partner across teams including Digital, SEO, Marketing Strategy, Social, Analytics and more to connect content strategy with broader customer and digital experiences.
- Help shape how content and product experiences work together to create seamless customer journeys.
- Identify opportunities to modernize content planning, creation, optimization, distribution, and measurement through AI\-enabled workflows.
- Develop scalable frameworks, playbooks, templates, modular content approaches, and ways of working that help teams effectively apply AI within content marketing.
- Help shape and advance the organization's approach to content marketing in an AI\-driven environment, identifying opportunities to scale successful practices, connect efforts across teams, and accelerate adoption of new ways of working.
- Connect AI\-related content efforts across lines of business, sharing learnings and identifying opportunities for greater consistency, efficiency, and impact.
- See around corners and define the CMCOE approach in a world of AI and LLMs, translating insights into actionable recommendations and content strategies.
- Use data and AI\-enabled analysis to better understand content performance and identify opportunities to improve engagement and business outcomes.
Consumer Content Strategy:
- Serve as the Content Strategy Lead for the Consumer line of business, translating business priorities into content programs that support business objectives.
- Identify opportunities to embed content within key business initiatives, customer journeys, product experiences, and digital experiences.
- Use audience insights, performance data, and emerging trends to shape strategy, improve effectiveness, and identify where content drives measurable business impact.
- Synthesize business priorities into actionable content plans and drive alignment across stakeholders.
- Operationalize content initiatives from strategy through delivery, ensuring consistency, quality, and speed to market.
- Drive stakeholder alignment and execution across a complex matrixed environment.
Required Qualifications:
- Turns ambiguity into action by stepping into undefined spaces, creating clarity, and building scalable strategies and frameworks from idea to implementation in matrixed environments
- Naturally curious and forward\-looking with a passion for emerging technologies, evolving customer behavior, and the future of content marketing.
- 10\+ years of experience in content marketing
- Strong content strategist with demonstrated experience translating business objectives, audience insights, and market trends into impactful content programs.
- Practical experience applying AI and LLMs to content planning, creation, optimization, research, and workflow improvement.
- Understanding of how AI, LLMs, evolving search behaviors, and emerging discovery experiences are reshaping content marketing and customer expectations.
- Experience using data, audience insights, and performance metrics to inform strategy and drive business outcomes.
- Experience developing content strategies across multiple channels and formats, including digital experiences, video, social, and emerging platforms.
- Success working in highly regulated environments and navigating complex stakeholder landscapes.
- Strong communication, presentation, and stakeholder management skills, with the ability to influence senior leaders and drive alignment across a matrixed organization.
- Demonstrated expertise using PowerPoint to communicate strategies, recommendations, and performance insights.
Skills:
- Collaboration
- Customer and Client Focus
- Decision Making
- Product Marketing and Branding
- Strategic Thinking
- Adaptability
- Analytical Thinking
- Digital Marketing
- Oral Communications
- Relationship Building
- Active Listening
- Business Acumen
- Coaching
- Leadership Development
- Performance Management
Minimum Education Requirement: High School Diploma / GED / Secondary School or equivalent
It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.
Internal employees who are currently working from home are still eligible to apply. However, if selected for the role, you may be required to work onsite in accordance with the workplace excellence policy.
Shift:
1st shift (United States of America)Hours Per Week:
40
Salary Context
This $124K-$184K range is below 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 Bank of America, 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 $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 ($154K) sits 28% below the category median. Disclosed range: $124K to $184K.
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.
Bank of America AI Hiring
Bank of America has 6 open AI roles right now. They're hiring across Data Engineer, AI Software Engineer, AI/ML Engineer. Positions span New York, NY, US, Addison, TX, US, Charlotte, NC, US. Compensation range: $182K - $230K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
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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