Senior Lead Software Engineer, Inference Platform Engineer

JPMorgan Chase

Confirmed live yesterday High trust

Quick summary

Work type: On-site
Location: Seattle, WA
Posted: 11 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

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Salary context

How this pay compares to similar roles

Similar $186k

$106k most similar roles pay here $248k

This listing doesn't post a salary. Most similar roles pay $151,000–$222,000.

Based on 240 similar postings.

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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 1117 open roles on FindRole.

Listed pay typically runs $186,160–$215,000 across 7 roles with salary data.

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At a glance

TL;DR · Senior Lead Software Engineer, Inference Platform Engineer

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As a Senior Lead Software Engineer- Inference platform engineer on the Infrastructure Platforms team, you will join an agile team to build and deliver secure, scalable technology products. You will architect and deploy cloud platforms optimized for AI/ML workloads, partner with AI teams to translate compute needs into infrastructure requirements, and manage cloud resources for performance and cost efficiency. Your daily work involves developing production code, building CI/CD pipelines, and implementing infrastructure-as-code to streamline machine learning operations. You will also drive the adoption of AI-assisted engineering practices and establish validation standards for secure coding. Required skills include experience with Kubernetes, Docker, Python, Go, Java, or C#, along with knowledge of transformer architecture, microservices, and NVIDIA GPU infrastructure software. You will utilize tools like Prometheus, Grafana, and MLflow to build high-performance LLM inference platforms.

Skills

Kubernetes Docker Python Go Java C# Infrastructure as Code CI/CD Cloud Computing Microservices ML Ops MLflow Prometheus Grafana vLLM NVIDIA GPU Infrastructure Transformer Architecture SDLC

What you'll do

Develop secure, high-quality production code while reviewing and debugging others' contributions.
Architect and deploy scalable cloud platforms optimized for AI/ML workloads.
Translate specific compute needs from AI teams into infrastructure requirements.
Build CI/CD pipelines and infrastructure-as-code to streamline machine learning deployments.
Monitor and optimize cloud resources for performance and cost efficiency.
Drive the adoption of approved AI-assisted engineering practices across development teams.
Establish measurable validation standards for secure coding, peer reviews, and automated testing.
Provide technical guidance and influence product design based on business goals.

What we're looking for

Formal training or certification in software engineering concepts and 5+ years of applied experience.
Hands-on experience in system design, application development, testing, and operational stability.
Strong experience with Kubernetes and containerization (Docker), including cluster operations and production troubleshooting.
Proficiency in at least one programming language such as Python, Go, Java, or C#.
Deep knowledge of cloud computing delivery models and infrastructure components like microservices, storage, and networking.
Experience with Infrastructure as Code and building CI/CD automation for ML deployment.
Foundational understanding of machine learning concepts, including transformer architecture, training, and inference.
Demonstrated experience leading the use of enterprise-authorized AI-assisted software development tools and ensuring responsible AI practices.

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