Lead Software Engineer - Python​/Go & AI​/ML

Position: Lead Software Engineer - Python / Go & AI/ML
At JPMorgan

Chase, we are building the infrastructure that powers the next generation of enterprise AI — and we need talented engineers who are passionate about LLM inference to help us do it. This is your opportunity to work at the intersection of cutting-edge machine learning and large-scale production systems, directly contributing to how one of the world's largest financial institutions deploys and optimizes AI  a Lead Software Engineer at JPMorgan

Chase within the AI/ML Data Platform team, you will be a key technical contributor on LLM inference performance — supporting optimization strategy, benchmarking, and efficiency  will collaborate closely with senior engineers and engineering leadership to help shape how our platform evolves, ensuring every model we serve is fast, cost-efficient, and production-ready. This is a high-impact individual contributor role where your technical contributions will have direct, measurable influence on the firm's AI capabilities.

Job Responsibilities Execute systematic benchmarking and performance characterization across production LLM workloads, establishing reproducible baselines, identifying regressions, and quantifying the impact of configuration changes before they reach production

Design and run quantization experiments — FP8, INT8/INT4 (GPTQ/AWQ), and next-generation precision formats — measuring accuracy delta, throughput improvement, memory reduction, and cost-per-token impact

Support speculative decoding strategy across the model portfolio, including draft model, n-gram, and multi-token prediction approaches, contributing to acceptance rate measurement and per-workload configuration recommendations

Build and maintain GPU efficiency metrics covering utilization, memory headroom, cost per 1K tokens, and waste identification — providing engineering teams with a data-driven view of platform efficiency

Benchmark the platform against external providers and published industry numbers, identifying gaps and contributing to improvement initiatives

Participate in inference engine upgrade evaluations, including new scheduler architectures, async tensor parallelism, disaggregated prefill/decode, and advanced speculative decoding, supporting systematic validation before production promotion

Contribute to GPU chaos engineering efforts, including induced failure scenarios, hardware diagnostic monitoring, and detection and recovery measurement

Leverage enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards

Apply knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation

Required qualifications, capabilities, and skills

Formal training or certification on software engineering concepts and advanced applied experience – preferably Go / Python Hands-on experience with LLM inference systems — vLLM, TensorRT-LLM, SGLang, LLM-D, or equivalent production serving engines

Strong understanding of GPU memory architecture, including KV cache sizing and dynamics, memory-bandwidth versus compute bottlenecks, and the practical implications of quantization at inference time

Experience with quantization techniques and their real-world tradeoffs at scale

Familiarity with speculative decoding and the variables that drive acceptance rates in production workloads

Rigorous benchmarking skills using GuideLLM, custom harnesses, or equivalent tooling, with the ability to support every performance claim with data Experience operating in cloud GPU infrastructure at scale (AWS, Kubernetes-based managed inference services)
Ability to communicate technical trade-offs clearly to engineering peers and senior stakeholders

Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, testing, troubleshooting, or documentation) with demonstrated ability to…