Bank of America is actively hiring for 4 AI and machine learning positions, concentrated in AI Product Manager (2) and AI Software Engineer (2) roles. Posted salary ranges span $217K - $217K, though only 25% of listings disclose compensation. The median posted ceiling sits at $217K. Positions are based in Jersey City, NJ, US, Chandler, AZ, US, Addison, TX, US. The most frequently requested skills across these postings are Kubernetes, Prompt Engineering, Mlflow, Python, Rag. Mid-level roles account for 100% of openings.
Skills & Technologies
Locations
Jersey City, NJ, US, Chandler, AZ, US, Addison, TX, US
Hiring by Role Category
Open Positions (4)
Product Manager - Tech Delivery - AI/ML Systems
Software Engineer – Golang, System Design, Kubernetes Platform Development & AI Automation
Software Engineer III-Generative AI Platform Engineering
Software Engineer III – Generative AI Platform Engineering
What Bank of America's hiring tells you
With 4 active AI role(s), this company is in the early exploration phase. That can mean either a pilot project being staffed up or a small embedded AI function inside a larger team. Worth investigating directly: ask the recruiter how the AI work is funded and who it reports to. Posted compensation range ($217K - $217K) suggests transparent and competitive pay practices.
The skill mix here leans toward Kubernetes in AI Product Manager roles. That is a clue about what Bank of America is building: teams hire for the work in front of them, not the work they wish they were doing.
Questions worth asking in the Bank of America interview loop
The signals above come from public job postings. The signals you actually need come from the conversation. A few questions calibrated to this company's tier:
- Is this AI work funded for at least 18 months, or is it tied to a specific project deadline?
- Will I be the only person doing this, or are there others I will collaborate with day to day?
- What does success look like at six months? At eighteen months?
Bank of America AI and ML Hiring
Bank of America has 4 active AI and ML roles in our dataset. Open positions span AI Product Manager, AI Software Engineer. Compensation ranges from $217K - $217K across disclosed roles. Roles are based in Jersey City, NJ, US, Chandler, AZ, US, Addison, TX, US.
Salary Benchmarks
The market median for AI roles is $218,800. AI Product Manager roles pay a median of $215,000 across the market. AI Software Engineer roles pay a median of $219,250 across the market. Top-quartile AI compensation starts at $272,100.
Skills Bank of America Looks For
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
AI Role Categories
AI Product Manager
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
Market compensation for AI Product Manager roles: $215,000 median across 227 positions with disclosed pay.
AI Software Engineer
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Market compensation for AI Software Engineer roles: $219,250 median across 372 positions with disclosed pay.
The AI Job Market Today
The AI job market spans 2,838 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,002), Data Scientist (256), AI Software Engineer (187). 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 (76) are outnumbered by mid-level (1,297) and senior (1,112) 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 353 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (426 positions), with 2,399 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 $218,800. Top-quartile roles start at $272,100, and the 90th percentile reaches $329,028. 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 $306,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,423 postings), Aws (831 postings), Azure (654 postings), Rag (638 postings), Gcp (459 postings), Pytorch (432 postings), Prompt Engineering (418 postings), Claude (369 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.
AI Hiring Overview
The AI job market has 2,838 open positions tracked in our dataset. By seniority: 76 entry-level, 1,297 mid-level, 1,112 senior, and 353 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (426 positions). The remaining 2,399 roles require on-site or hybrid attendance.
The market median for AI roles is $218,800. Top-quartile compensation starts at $272,100. The 90th percentile reaches $329,028. Highest-paying categories: AI Safety ($306,000 median, 19 roles); Research Engineer ($300,000 median, 133 roles); AI Architect ($254,798 median, 61 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Frequently Asked Questions
Frequently Asked Questions
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