Principal Software Engineer - LLM Optimization

At 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.

JPMorganChase, 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

[https://careers.jpmorgan.com/us/en/how-we-hire/faqs] for more information about

requesting an accommodation.

JPMorgan Chase & Co. is an Equal Opportunity Employer, including

Disability/Veterans

Our 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.