JPMorgan Chase
Applied AI/ML Lead - Vice President - Payments
$171K–$260K
New York, NY +2 · Director · Full-time · Posted May 12, 2026
- Location
- New York, NY
Palo Alto, CA
Seattle, WA - Experience
- 6+ years of industry experience delivering applied ML, or PhD with equivalent applied experience.
- Education
- Master’s or PhD in Computer Science, Machine Learning, Statistics, Mathematics, Operations Research, or related field.
About the job
Leads hands-on delivery of production AI/ML capabilities across payments, applying neural networks to improve fraud and risk outcomes, operational automation, and client experience.
What you'll do
- Own an AI/ML problem area from opportunity sizing and problem framing through production rollout and iteration.
- Design, build, evaluate, and operate production ML systems, including model monitoring, governance, and performance improvement.
- Set technical direction and lead execution through partnership, reviews, mentoring, and communication with stakeholders.
What they ask for
- Master’s or PhD in a related field, plus 6+ years of industry experience delivering applied ML; alternatively, a PhD with equivalent applied experience.
- Track record of shipping ML models into production with measurable business impact and post-launch iteration.
- Hands-on expertise in neural networks and Transformers, including fine-tuning, distillation or compression, and robust evaluation.
- Strong Python and PyTorch or TensorFlow skills; experience with SQL, distributed processing, and AWS or equivalent cloud.
Nice to have
- Payments domain experience.
- Experience with streaming or real-time architectures, ML platforms/MLOps, Docker/Kubernetes, or Databricks and Snowflake.
Pay
USD 171,000.00-$260,000.00 base salary (New York, NY); USD 171,000.00-$260,000.00 base salary (Palo Alto, CA); USD 171,000.00-$260,000.00 base salary (Seattle, WA)
Tools
Transformers, Python, PyTorch, TensorFlow, SQL, Spark/PySpark, AWS, Docker/Kubernetes
AI focus
Build, fine-tune, distill, and deploy neural-network models—including Transformers—for fraud and risk, anomaly detection, payment automation, and routing or authorization optimization.
Model-written summary · the original posting is authoritative