About this role
Salary: £62,000 - 102,000 per year
Requirements: Formal training or certification on software engineering concepts and advanced applied experience, preferably in Go or PythonHands-on experience with LLM inference systems such as 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, including AWS and Kubernetes-based managed inference servicesAbility 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, 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 and outputs, and adherence to resiliency and security expectationsPreferred: Experience with disaggregated prefill/decode serving architecturesPreferred: Familiarity with GPU hardware diagnostics tools such as DCGM, NVML, or XID event trackingPreferred: Experience with ML observability and production monitoring for inference workloadsPreferred: Awareness of the LLM inference competitive landscape with a track record of applying industry benchmarks to drive platform improvements 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 productionDesign and run quantization experiments, including 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, 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 Technologies: AIAWSCloudHardwareSupportKubernetesLLMMarketingPythonSecurityvLLMMachine Learning More:
At JPMorganChase, we are building the infrastructure that powers the next generation of enterprise AI, and this Lead Software Engineer role sits within our AI/ML Data Platform team. We are focused on LLM inference performance, optimization strategy, benchmarking, and efficiency at scale, and this high-impact individual contributor position works closely with senior engineers and engineering leadership to shape how our platform evolves. We offer the opportunity to contribute directly to how one of the worlds largest financial institutions deploys and optimizes AI at scale, within a global organization that values diversity, inclusion, and long-term client partnerships. Our Corporate Functions teams support the business across finance, risk, human resources, marketing, and other essential areas, and this is a full-time role.
last updated 36 week of 2026