Lead Software Engineer - Python / Go & AI/ML

United Kingdom | Aug. 31, 2026

Report as Closed

Company: JPMorgan Chase & Co.

Type: Onsite

Employment: Full Time

Description: At JPMorganChase, 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 at scale. As a Lead Software Engineer at JPMorganChase within the AI/ML Data Platform team, you will be a key technical contributor on LLM inference performance — supporting optimization strategy, benchmarking, and efficiency at scale. You 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 ResponsibilitiesExecute systematic benchmarking and performance characterization across production LLM workloads, establishing reproducible baselines, identifying regressions, and quantifying the impact of configuration changes before they reach productionDesign 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 impactSupport 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 recommendationsBuild 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 efficiencyBenchmark the platform against external providers and published industry numbers, identifying gaps and contributing to improvement initiativesParticipate in inference engine upgrade evaluations, including new scheduler architectures, async tensor parallelism, disaggregated prefill/decode, and advanced speculative decoding, supporting systematic validation before production promotionContribute to GPU chaos engineering efforts, including induced failure scenarios, hardware diagnostic monitoring, and detection and recovery measurementLeverage 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 standardsApply 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 skillsFormal 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 enginesStrong understanding of GPU memory architecture, including KV cache sizing and dynamics, memory-bandwidth versus compute bottlenecks, and the practical implications of quantization at inference timeExperience with quantization techniques and their real-world tradeoffs at scaleFamiliarity with speculative decoding and the variables that drive acceptance rates in production workloadsRigorous benchmarking skills using GuideLLM, custom harnesses, or equivalent tooling, with the ability to support every performance claim with dataExperience 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 stakeholdersHands-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 critically evaluate and validate AI-generated outputsUnderstanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations Preferred qualifications, capabilities, and skillsExperience with disaggregated prefill/decode serving architecturesFamiliarity with GPU hardware diagnostics tools such as DCGM, NVML, or XID event trackingExperience with ML observability and production monitoring for inference workloadsAwareness of the LLM inference competitive landscape with a track record of applying industry benchmarks to drive platform improvements J.P. Morgan is a global leader in financial services, providing strategic advice and products to the world’s most prominent corporations, governments,

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