Applied AI/ML Lead - Vice President - Payments

JPMorgan Chase & Co.; New York; Hybrid; USD 180,000 - 300,000; Full time

Job summary

JPMorgan Chase Paymentsenchances how organizations execute secure, cross-border payments by deploying AI/ML capabilities at scale. The VP Applied AI/ML Lead will own end-to-end delivery, design production ML systems, and drive governance, latency, and reliability across teams.

You will fine-tune transformers, apply distillation, and lead model deployment while partnering with Product, Engineering, Risk, Compliance, and Data teams to deliver measurable business impact.

Qualifications

• Master’s or PhD in CS/ML/Statistics/Math/OR or related field
• Proven track record shipping ML models into production with measurable business impact
• Strong hands-on expertise in neural networks and Transformers with practical experience in fine-tuning, distillation, and compression
• Strong software engineering skills in Python, with experience in PyTorch or TensorFlow and ML libraries
• Experience building data-driven systems using SQL and distributed processing (Spark/PySpark)
• Cloud and production experience on AWS, deploying services and pipelines at scale
• Ability to turn ambiguous problems into structured ML plans (data strategy, modeling, evaluation, rollout)

Responsibilities

• Own major AI/ML problem areas end-to-end from framing to production rollout
• Design and build production-grade ML systems that operate at payments scale
• Develop state-of-the-art neural approaches including Transformers and representation learning
• Define rigorous evaluation metrics, calibration, robustness, and online experimentation
• Establish model lifecycle standards: reproducibility, testing, monitoring, drift detection
• Partner with Risk/Compliance for governance, explainability, and audit readiness
• Lead through influence: design/code reviews, mentoring, setting standards
• Communicate tradeoffs clearly to senior stakeholders

Skills

Python

PyTorch or TensorFlow

SQL

Spark/PySpark

AWS

Docker/Kubernetes

ML platform/MLOps

Communication with stakeholders

Education

Master’s or PhD in Computer Science, Machine Learning, Statistics, Mathematics, Operations Research, or related field

Tools

Databricks

Snowflake

Job description

As part of the Commercial & Investment Bank, JPMorgan Chase Paymentsenables organizations of all sizes to execute transactions efficiently and securely, transforming the movement of information, money, and assets. We tackle complex challenges across the payments lifecycle with solutions that facilitate seamless transactions across borders, industries, and platforms. Operating in over 160 countries and handling more than 120 currencies, we are a leading processor of USD payments with daily transaction volumes in the trillions.

As aVice President, Applied AI/ML Lead (Sr Level IC role)within JPMorgan Chase Payments, you willown and drivethe end-to-end delivery of high-impact AI/ML capabilities across the payments ecosystem—while remaining deeply hands‑on. You will applymodern neural networks (including Transformers)to build, fine‑tune, distill, and deploy models that improvefraud/risk outcomes, operational automation, and client experience, operating under real-world constraints (latency, scale, reliability, explainability, and governance). You will set technical direction, establish standards, and lead execution through influence—partnering closely with Product, Engineering, Risk, Compliance, and Data teams.

Job responsibilities

• Own a major AI/ML problem area end-to-end(e.g., transaction risk & fraud, anomaly detection, payment exceptions automation, routing/authorization optimization), from opportunity sizing and problem framing through production rollout and iteration.
• Design and build production-grade ML systemsthat operate at payments scale, balancing accuracy, latency, throughput, and cost across batch, near-real-time, and real-time inference patterns.
• Develop state-of-the-art neural approachesincludingTransformer architectures, representation learning, and sequence/graph methods where appropriate; applyfine-tuning(full/parameter-efficient),distillation, and model compression techniques to meet deployment constraints.
• Define rigorous evaluation and measurement: offline metrics, calibration, robustness testing, segmentation, and online experimentation where feasible; translate model lift into business impact (loss reduction, approval rates, false-positive reduction, ops productivity, client outcomes).
• Establish model lifecycle standards: reproducibility, testing, monitoring/alerting, drift detection, champion–challenger approaches, incident response/rollback, and ongoing performance governance.
• Partner with Risk/Compliance and model governance stakeholdersto ensure appropriate documentation, controls, explainability/interpretability as required, and audit-ready processes.
• Lead through influence: drive technical decisions via design reviews, code/model reviews, and mentoring; raise the bar across applied scientists and ML engineers through best practices and pragmatic standards.
• Communicate clearly to senior stakeholders, including tradeoffs, limitations, and recommended actions; make complex model behavior and risk/benefit understandable and decision-ready.

Required qualifications, capabilities, and skills

• Master’s or PhDin Computer Science, Machine Learning, Statistics, Mathematics, Operations Research, or related field, plus6+ yearsof industry experience delivering applied ML (or PhD with equivalent applied experience).
• Demonstrated track record ofshipping ML models into productionwith measurable business impact, including iteration post-launch (monitoring, retraining, recalibration, and continuous improvement).
• Strong hands‑on expertise ineural networks and Transformers, including practical experience with:
  • Fine-tuningstrategies (e.g., full fine‑tuning and parameter‑efficient methods)
  • Distillation / compression(teacher–student, quantization‑aware approaches, latency/cost-driven optimization)
  • Robustevaluationand failure-mode analysis for real‑world deployment
• Strong software engineering skills inPython, with deep experience inPyTorch or TensorFlowand standard ML/data libraries.
• Experience building data-driven systems usingSQLand distributed processing (e.g.,Spark/PySparkor equivalent).
• Cloud and production experience onAWS(or equivalent cloud), including deploying services/pipelines and operating them reliably at scale.
• Ability to take ambiguous business problems and turn them intostructured ML plans: data strategy, modeling approach, evaluation, rollout, and operationalization.
• Excellent communication skills, including explaining tradeoffs (accuracy vs latency, risk vs customer friction, complexity vs maintainability) to both technical and non-technical audiences.

Preferred qualifications, capabilities, and skills

• Payments domain experience:fraud/risk, transaction monitoring, identity/account takeover, disputes/chargebacks, sanctions/AML-adjacent signal work, payment exceptions and investigations, routing/authorization optimization, or treasury/transaction banking.
• Experience withstreaming/real-timearchitectures and feature generation (e.g., event-driven systems, point-in-time correctness, leakage prevention).
• Strong ML platform/MLOps exposure: feature stores, model registries, CI/CD for ML, scalable training/inference, observability, and governance workflows.
• Experience withDocker/Kubernetesand modern data platforms (e.g.,Databricks,Snowflake) where relevant.