below marketmost similar roles pay hereabove market
This listing doesn't post a salary. Most similar roles pay
$170,250–$217,425.
Based on 240 similar postings.
Employer
About JPMorgan Chase
JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.
JPMorgan Chase currently has
1226 open roles
on FindRole.
Listed pay typically runs
$156,750–$215,000
across 5 roles with salary data.
JOB TITLE: Principal Software Engineer- Core AI Platform
As a Principal Software Engineer on the Core AI Infrastructure Platform team, you will design and promote a shared ecosystem unifying training and inference pipelines across hybrid-cloud and Neo-cloud environments. You will architect solutions to enhance reliability and scalability for AI/ML platforms while building reusable platform services, APIs, SDKs, agents, and libraries. Your daily work involves implementing AI Foundation Services, owning non-functional requirements like observability and security controls, and establishing reference architectures for operational readiness. You must possess over 10 years of experience and strong hands-on coding skills in Python. You will solve deep infrastructure challenges by managing distributed systems, cloud platforms, and machine learning workloads. Key responsibilities include mentoring engineers, participating in on-call rotations, and ensuring responsible AI use through secure data handling and rigorous testing.
What does a Software Engineer earn in Washington?
Median $202800 from 409 postings across 38 companies.
Architect and design solutions to enhance the reliability and scalability of AI/ML platforms.
Build reusable platform services, APIs, SDKs, and libraries to standardize model hosting and inference.
Implement AI Foundation Services capabilities to support delivery from technical design through operational support.
Develop tooling for observability, resilience, security controls, infrastructure management, and cost optimization.
Establish and enforce standards for reliability, automation, and operational readiness across all services.
Participate in on-call rotations to debug and resolve complex production issues and systemic gaps.
Mentor engineers to improve engineering quality, documentation, and operational rigor.
What we're looking for
Formal training or certification on software engineering concepts and 10+ years of applied experience.
Strong hands-on coding experience in Python with experience delivering production-grade services.
Demonstrated experience leading the use of approved AI-assisted software development tools for coding, testing, and troubleshooting.
Strong understanding of responsible AI use, data sensitivity, and secure handling of inputs/outputs in engineering workflows.
Hands-on practical experience with system design, automated testing, debugging, and operational stability for production software.
Experience implementing observability, logging, metrics, alerts, SLOs, incident response, and root-cause analysis for production services.
Working knowledge of software application development with depth in cloud platforms, AI/ML platforms, distributed systems, or infrastructure engineering.
Proven skills in managing AI infrastructure on cloud platforms, including deployment, scaling, and optimizing machine learning workloads (preferred).
Python
Java
Scala
Go
MLOps
LLM
Agentic AI
CI/CD
Distributed Systems
IaC
Containers
Ray
REST
System Design
Data Engineering
Platform Engineering
Observability
RLHF
DPO
SRE
Python
Terraform
AWS
Kubernetes
Infrastructure as Code
CI/CD
AI
ML
Distributed Systems
Cloud-native Architecture
Observability
Automation
fin-ops
Incident Management
Data Engineering
Python
Go
Java
C++
Kubernetes
OCI
Distributed Systems
Machine Learning
LLMs
CI/CD
PyTorch
TensorFlow
Hugging Face Transformers
Microservices
Event-driven Systems
Infrastructure Automation
Service Mesh
APIs
Data Modeling
Python
C++
Java
C#
JavaScript
vLLM
SGLang
Ray
VERL
Slime
GPU Computing
Distributed Training
ML Systems
AI Infrastructure
Data Analytics
Disaggregated Inference
Reinforcement Learning