Job DescriptionAt JPMorganChase, we are building the infrastructure that powers the next generation of enterprise AI - and we need the best minds in 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 influencing how one of the world's largest financial institutions deploys and optimizes AI at scale.
As a Principal Software Engineer at JPMorganChase within the AI/ML Data Platform team, you will serve as the firm's deepest technical voice on LLM inference performance - owning optimization strategy, benchmarking rigor, and efficiency at scale. You will work directly with senior engineering leadership to shape how our platform evolves, ensuring every model we serve is fast, cost-efficient, and production-ready. This is a high-visibility individual contributor role where your technical decisions will have direct, measurable impact on the firm's AI capabilities
Job Responsibilities - Own systematic benchmarking and performance characterization across all production LLM workloads. Establish reproducible baselines, catch regressions early, and quantify the impact of every configuration change before it touches production
- Design and execute quantization experiments - FP8, INT8/INT4 (GPTQ/AWQ), next-generation precision formats on current hardware - measuring accuracy delta, throughput improvement, memory reduction, and cost-per-token impact
- Drive speculative decoding strategy across the model portfolio: draft model, n-gram, and multi-token prediction approaches. Own acceptance rate measurement and per-workload configuration recommendations
- Build and maintain a GPU efficiency scorecard: utilization, memory headroom, cost per 1K tokens, and waste identified - giving leadership a data-driven view of platform efficiency at all times
- Benchmark our platform against external providers and published industry numbers - know what good looks like, and close the gap
- Lead inference engine upgrade evaluations: new scheduler architectures, async tensor parallelism, disaggregated prefill/decode, advanced speculative decoding - systematic validation before production promotion
- Collaborate with the EKS and disaggregated serving teams on KV-cache optimization, prefix caching strategies, and multi-node serving architecture
- Design and run GPU chaos engineering: induced failure scenarios, hardware diagnostic monitoring, detection and recovery measurement
- Architect and govern agentic AI-enabled engineering workflows (using enterprise-authorized tools within the work environment) to improve delivery speed, code quality, and operational outcomes at scale (e.g., AI-driven PR review assistance, test generation/maintenance, release readiness checks, incident triage and root-cause acceleration), while defining guardrails for validation, security, resiliency, and reuse across teams.
- 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 at scale.
Required qualifications, capabilities, and skills - Formal training or certification on software engineering concepts and 7+ years applied experience
- Deep, hands-on experience with LLM inference systems - vLLM, TensorRT-LLM, SGLang, LLM-D or equivalent production serving engines
- Strong grasp of GPU memory architecture: KV cache sizing and dynamics, memory-bandwidth vs compute bottlenecks, 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 instincts - GuideLLM, custom harnesses, or equivalent. Every claim has a number behind it
- Comfort operating in cloud GPU infrastructure at scale (AWS; EKS, managed inference services)
- Demonstrated awareness of the LLM inference competitive landscape, with a track record of applying industry benchmarks to drive platform improvements communicate technical trade-offs clearly to senior engineering and business stakeholders - this role presents upward regularly
- Demonstrated experience designing and leading adoption of agentic AI-enabled development practices (using enterprise-authorized tools within the work environment) across teams, including setting standards for human-in-the-loop validation, auditability/traceability of changes, and secure handling of sensitive data.
- Strong understanding of responsible AI use and control expectations in engineering workflows, including security/resiliency implications, data sensitivity, and risk-based governance; ability to influence senior technical leaders on safe scaling patterns and reuse.
Preferred qualifications, capabilities, and skills - Experience with disaggregated prefill/decode serving architectures, GPU hardware diagnostics (DCGM/NVML/XID event tracking), ML observability and production monitoring
FEDERAL DEPOSIT INSURANCE ACT: This position is subject to Section 19 of the Federal Deposit Insurance Act. As such, an employment offer for this position is contingent on JPMorganChase's review of criminal conviction history, including pretrial diversions or program entries.
About UsJPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world's most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.
We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
JPMorgan Chase & Co. is an Equal Opportunity Employer, including Disability/Veterans
About the TeamOur professionals in our Corporate Functions cover a diverse range of areas from finance and risk to human resources and marketing. Our corporate teams are an essential part of our company, ensuring that we're setting our businesses, clients, customers and employees up for success.
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