Senior Lead Software Engineer- AI/ML Training Infrastructure
About The Position
As a Senior Lead Software Engineer at JPMorganChase within the Corporate Sector, Infrastructure Platforms team, you will design, build, and operate secure, scalable, and resilient infrastructure platforms that power enterprise AI/ML workloads. You will help deliver technology products securely, reliably, and with high availability while contributing technical expertise, engineering leadership, and problem-solving capabilities. You will partner with AI/ML engineering teams, platform teams, product owners, and infrastructure stakeholders to translate compute, storage, networking, GPU, and scalability requirements into production-ready platforms for multi-GPU and multi-node model training. You will also advance automation, developer productivity, responsible AI-assisted engineering practices, and operational excellence across the software delivery lifecycle. The role includes technical leadership for engineers, contractors, and vendor partners; contributing to architecture and infrastructure strategy; optimizing cloud and GPU resources; and promoting secure, reliable engineering practices and approved AI-assisted development across teams. Employment offers are contingent on JPMorgan Chase’s review of criminal conviction history under Section 19 of the Federal Deposit Insurance Act.
Requirements
- Formal training or certification in software engineering concepts and at least 5 years of applied experience.
- Hands-on experience building highly scalable and highly available infrastructure for machine learning training and/or inference workloads.
- System-level understanding of GPU infrastructure, accelerators, high-speed interconnects, distributed compute, and related platform technologies.
- Strong experience with Kubernetes and containerization technologies, including Docker, cluster operations, workload scheduling, observability, and production troubleshooting.
- Proficiency in at least one modern programming language, such as Python, Go, Java, or C#.
- Ability to independently solve complex design, scalability, reliability, performance, and functionality challenges with minimal oversight.
- Deep understanding of cloud component architecture, including microservices, compute, storage, networking, security, routing, and switching technologies.
- Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools for activities such as coding, code review, test acceleration, and troubleshooting, and setting expectations for validating AI outputs for correctness, performance, and security.
- Strong understanding of responsible AI use in engineering workflows, including data sensitivity, secure handling of inputs and outputs, and compliance with resiliency and security expectations; experience coaching senior engineers or leads on compliant usage patterns and controls.
- Ability to design, develop, test, and deliver secure, high-quality production code and review, debug, and improve code written by others.
Nice To Haves
- Hands-on experience with performance monitoring, production debugging, profiling, sampling, bottleneck analysis, and capacity optimization.
- Foundational understanding of NVIDIA GPU infrastructure software and ecosystem tooling, such as DCGM, BCM, NVIDIA drivers, CUDA, and training libraries.
- Experience with MLOps platforms and tooling, including MLflow or similar model lifecycle management solutions.
- Background in high-performance computing, distributed systems, and ML frameworks, including distributed training, Ray.io, Slurm, or similar workload orchestration technologies.
- Strong knowledge of network architecture, including high-throughput and low-latency networking patterns for distributed compute and AI/ML workloads.
- Familiarity with cloud data services, big data processing platforms, Linux systems, and storage technologies used in large-scale data and ML environments.
- Experience designing platforms that support model training, experiment tracking, feature pipelines, model serving, and scalable inference in enterprise environments.
Responsibilities
- Design, develop, test, and deliver secure, high-quality production code; review, debug, and improve code written by others.
- Architect, build, and operate secure, scalable cloud infrastructure platforms optimized for multi-GPU and multi-node AI/ML training workloads.
- Partner with AI/ML, data science, and platform engineering teams to translate compute, storage, networking, GPU, and scalability needs into infrastructure requirements and platform capabilities.
- Drive technical design decisions influencing product architecture, application functionality, infrastructure strategy, and operational effectiveness.
- Monitor, manage, and optimize cloud and GPU infrastructure resources for performance, reliability, utilization, scalability, and cost efficiency.
- Build and maintain CI/CD pipelines, automation frameworks, and infrastructure-as-code solutions for ML platform deployment, operations, and lifecycle management.
- Provide technical leadership and guidance to engineers, contractors, and vendor partners, ensuring solutions align with business priorities, engineering standards, security expectations, and long-term platform strategy.
- Use the Software Development Life Cycle toolchain, including enterprise-approved AI-assisted development and automation capabilities, to improve productivity and automation at scale.
- Champion firmwide SDLC frameworks, engineering standards, secure coding practices, resiliency expectations, and operational best practices.
- Drive adoption and governance of approved AI-assisted engineering practices, establish validation standards for secure coding, peer review, and automated testing, and promote reuse of proven patterns and automation within the SDLC/TLM toolchain.
- Apply knowledge of SDLC tools and approved AI-assisted development and automation capabilities to improve the value realized through automation at scale.
Benefits
- Competitive total rewards package, including base salary determined by role, experience, skill set, and location.
- Eligible roles may receive commission-based pay and/or discretionary incentive compensation in cash and/or forfeitable equity.
- Comprehensive health care coverage.
- On-site health and wellness centers.
- Retirement savings plan.
- Backup childcare.
- Tuition reimbursement.
- Mental health support.
- Financial coaching.
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
