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 is core to how we drive Responsible Growth. This includes our commitment to being an inclusive workplace, attracting and developing exceptional talent, supporting our teammates’ physical, emotional, and financial wellness, recognizing and rewarding performance, and how we make an impact in the communities we serve.
Bank of America is committed to an in-office culture with specific requirements for office-based attendance and which allows for an appropriate level of flexibility for our teammates and businesses based on role-specific considerations.
At Bank of America, you can build a successful career with opportunities to learn, grow, and make an impact. Join us!
Job Description:
Bank of America's Global Information Security (GIS) team is seeking a Cyber Threat Defense Sr AI/ML Engineer to build and integrate advanced AI and machine learning capabilities into our cyber defense ecosystem. This person will drive innovation across preventative, detective, and responsive security controls by engineering the full spectrum of intelligent automation: deterministic and scripted automation, custom machine learning models, large language models (LLMs), and agentic AI systems. This is a senior individual contributor role balancing hands-on engineering with technical leadership, working closely with leadership and engineering teams to drive AI integration into cyber defense.
This engineer will focus on applying AI to defend against modern threats, including threat actors who are themselves leveraging AI, and will partner with security subject matter experts across GIS on defending the bank's own use of AI. The ideal candidate is an experienced AI/ML engineer with deep fundamentals in machine learning theory and practice, strong production engineering discipline, and a working understanding of cybersecurity, who knows that the right solution is sometimes a script, sometimes a model, and sometimes an agent.
Role Responsibilities- Design, build, and deploy AI-powered capabilities for threat hunting, anomaly detection, and automated incident response over large-scale security telemetry.
- Develop and operationalize custom machine learning models and LLM-based workflows tailored to cybersecurity use cases, owning the lifecycle from data preparation and feature engineering through training, evaluation, deployment, and monitoring.
- Match the technique to the problem: apply deterministic automation, classical machine learning, or generative AI (or a combination) based on the problem structure, the available data, and the operational risk profile.
- Build LLM-based tooling that multiplies analyst effectiveness, such as investigation support, detection engineering assistance, and knowledge retrieval.
- Partner with GIS operational and technical teams to identify opportunities for AI-driven enhancements to security controls and architecture.
- Prototype and evaluate emerging AI technologies for applicability in cyber threat detection and response.
- Collaborate with offensive security teams to develop AI-enhanced red teaming and adversarial emulation capabilities.
- Contribute to architectural decisions that support scalable, well-governed AI integration across GIS security controls.
- Promote responsible and ethical use of AI in security operations, partnering with model governance stakeholders on bias mitigation and explainability.
- Act as a technical expert on AI-driven cybersecurity initiatives, advising senior leadership and mentoring engineers and analysts.
- 7+ years of hands-on machine learning engineering experience, including fine-tuning, evaluating, and deploying custom models in production.
- Strong command of machine learning fundamentals, including model training and evaluation (model weights, loss functions, precision, recall, F1, calibration), feature engineering, embeddings, and real-world data issues such as class imbalance, label noise, and model drift. Candidates whose AI experience consists primarily of using generative AI tools, agents, or APIs will not meet this bar; candidates should expect to discuss models they have personally trained and evaluated.
- Proficiency in Python and hands-on experience with ML frameworks such as PyTorch or scikit-learn, including model evaluation harnesses and experiment tracking.
- Hands-on experience building LLM-powered applications and agentic AI systems (e.g., retrieval-augmented generation, fine-tuning, tool use, orchestration), grounded in the ML fundamentals above.
- Experience delivering production systems at scale involving data pipelines, model deployment, MLOps, and automation.
- Experience with enterprise cloud AI development platforms (e.g., Azure AI Foundry, Amazon Bedrock, Google Cloud Vertex AI) or equivalent open-source or self-hosted model infrastructure.
- Working understanding of cybersecurity fundamentals (the attack lifecycle, common attacker techniques, and defensive controls) and of how AI can enhance defensive operations.
- Familiarity with AI governance and model risk management concepts, such as model validation, explainability, and responsible AI.
- Strong communication and presentation skills, including the ability to translate complex technical concepts for senior executives and cross-functional stakeholders.
- Bachelor's degree in computer science, a related quantitative field, or equivalent applied experience; advanced degree (MS/PhD) preferred.
- Experience applying ML or AI to cybersecurity problems such as detection engineering, threat hunting, malware analysis, or security automation.
- Experience working with security telemetry at scale (e.g., EDR, SIEM, network, or identity data).
- Understanding of offensive security tactics and threat actor behaviors, and of how AI can enhance red teaming, attack path mapping, and threat modeling.
- Hands-on offensive security experience (e.g., CTF competitions, red team tooling development, or published security research).
- Experience with the open-source LLM ecosystem (e.g., Hugging Face, LangChain), LLM guardrails, and agent orchestration frameworks.
- Familiarity with adversarial machine learning and AI security risks.
- Experience with AI-enhanced SOAR (Security Orchestration, Automation, and Response) platforms.
- Prior work in regulated industries, including model risk and compliance considerations in financial services.
Skills:
- Artificial Intelligence
- Critical Thinking
- Threat Analysis
- Cyber Security
- Data Privacy and Protection
- Data and Trend Analysis
- Stakeholder Management
This job will be open and accepting applications for a minimum of seven days from the date it was posted.
Shift:
1st shift (United States of America)Hours Per Week:
40Pay Transparency details
US - CO - Denver - 1144 15th St - Denver Gis (CO9926), US - DC - Washington - 1800 K St NW - 1800 K Street NW (DC1842), US - IL - Chicago - 540 W Madison St - Bank Of America Plaza (IL4540), US - MA - Boston - 100 Federal St - 100 Federal St Lp (MA5100), US - NJ - Jersey City - 101 Hudson St - 101 Hudson (NJ2101)Pay and benefits informationPay range$145,000.00 - $192,500.00 annualized salary, offers to be determined based on experience, education and skill set.Discretionary incentive eligibleThis role is eligible to participate in the annual discretionary plan. Employees are eligible for an annual discretionary award based on their overall individual performance results and behaviors, the performance and contributions of their line of business and/or group; and the overall success of the Company.BenefitsThis role is currently benefits eligible. We provide industry-leading benefits, access to paid time off, resources and support to our employees so they can make a genuine impact and contribute to the sustainable growth of our business and the communities we serve.Skills Required
- 7+ years of hands-on machine learning engineering experience, including fine-tuning, evaluating, and deploying custom production models
- Strong knowledge of machine learning fundamentals, model training and evaluation, feature engineering, embeddings, class imbalance, label noise, and model drift
- Proficiency in Python and hands-on experience with PyTorch or scikit-learn, including evaluation harnesses and experiment tracking
- Experience building LLM-powered applications and agentic AI systems, including retrieval-augmented generation, fine-tuning, tool use, and orchestration
- Experience delivering production systems at scale involving data pipelines, model deployment, MLOps, and automation
- Experience with enterprise cloud AI platforms such as Azure AI Foundry, Amazon Bedrock, or Google Cloud Vertex AI, or equivalent model infrastructure
- Working understanding of cybersecurity fundamentals, attacker techniques, the attack lifecycle, and defensive controls
- Familiarity with AI governance and model risk management, including validation, explainability, and responsible AI
- Strong communication and presentation skills for technical, executive, and cross-functional audiences
- Bachelor's degree in computer science, a related quantitative field, or equivalent applied experience
- Advanced degree such as an MS or PhD
- Experience applying AI or machine learning to cybersecurity, including detection engineering, threat hunting, malware analysis, or security automation
- Experience with security telemetry at scale, including EDR, SIEM, network, or identity data
- Understanding of offensive security tactics, threat actor behavior, red teaming, attack path mapping, or threat modeling
- Hands-on offensive security experience, such as CTF participation, red team tooling, or published security research
- Experience with Hugging Face, LangChain, LLM guardrails, and agent orchestration frameworks
- Familiarity with adversarial machine learning and AI security risks
- Experience with AI-enhanced SOAR platforms
- Experience in regulated industries with model risk and compliance considerations
Bank of America Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Bank of America and has not been reviewed or approved by Bank of America.
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Healthcare Strength — Health coverage is described as comprehensive, with medical, dental, vision, virtual care via Teladoc, wellness programs, and specialized support for cancer and menopause. Wellness credits and an always‑on EAP with in‑person sessions add to the depth of care.
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Parental & Family Support — New parents can access up to 26 weeks of leave, including 16 weeks fully paid for eligible teammates, alongside back‑up child and adult care. Family‑building resources and reimbursements (e.g., fertility, adoption, surrogacy) and a dedicated Life Event Services team extend support across life stages.
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Equity Value & Accessibility — Broad‑based equity through the Sharing Success program, including $1B in stock to nearly all non‑executive employees in January 2026, is intended to foster an ownership mindset. Stock awards (including RSUs) are a recurring component that aligns employees’ interests with shareholders.
Bank of America Insights
What We Do
We make financial lives better for our clients and our communities through the power of every connection. Our employees are at the heart of this purpose, and are key to driving responsible growth. Every day, across the globe, our employees bring a commitment to our purpose and to driving responsible growth by living our values: deliver together, act responsibly, realize the power of our people and trust the team. A key aspect of driving responsible growth is doing so in a sustainable manner, a critical pillar of which is being a great place to work for our teammates.
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