Sr. Principal Software Engineer

Cerence; United States; Hybrid; USD 185,000 - 280,000; Full time

Benefits offered by this job

Annual bonus opportunity

Insurance coverage (medical, dental,…)

Paid time off

Paid holidays

RRSP contribution

Equity awards

Remote/hybrid work options

Job summary

Cerence Inc. is seeking a Senior Principal Software Engineer in the United States to lead optimization of ML inference pipelines across data center, edge, and embedded targets.

You will drive quantisation, cache optimization, and runtime improvements to deliver low latency and high throughput for Cerence’s AI-powered mobility solutions. The role demands deep GPU hardware knowledge, hands-on CUDA development, and proven production deployment experience, with a focus on scalable,

Qualifications

• Proven experience optimizing ML inference performance in production.
• Deep understanding of GPU architecture and memory hierarchies.
• Hands-on experience with CUDA and low-level performance tuning.
• Experience deploying models beyond research environments.

Responsibilities

• Optimize and deploy high-performance ML inference pipelines.
• Own inference runtimes across data center, edge, and embedded platforms.
• Push model performance through quantisation, kernel fusion, and cache optimization.
• Drive latency and throughput improvements that directly impact production products.
• Enable efficient, reliable deployment without external vendor dependency.

Skills

ML inference optimization

GPU architecture

CUDA

Low-level performance tuning

Production deployment

Tools

vLLM

TensorRT-LLM

llama.cpp

QAIRT

Job description

A Moving Experience.

Who is Cerence AI?

Cerence AI is the global leader in AI for transportation, specialized in building AI and voice-powered companions for cars, two-wheelers, and more that enable people to focus on what matters most. With over 500 million cars shipped with Cerence AI’s technology, we partner with leading automakers (such as Volkswagen, Mercedes, Audi, Toyota and many more), mobility providers, and technology companies to power intuitive, integrated experiences that create safer, more connected, and more enjoyable journeys for drivers and passengers alike.

Our Driving Force

Our team is dedicated to pushing the boundaries of AI innovation, working around the globe with headquarters in Burlington, Massachusetts, USA and 16 other offices across Europe, Asia, and North America. We bring together diverse backgrounds, and varied skill sets with the shared goal of advancing the next generation of transportation user experiences. Our culture is customer-centric, collaborative, fast-paced, and fun, with continuous opportunities for learning and development to support your career growth.

Interested in having a significant impact in a dynamic industry with a high-performing global team? We’re looking for an exceptional SeniorPrincipalSoftware Engineer who is ready to drive the future of mobility with us!

Job Description:

What You Will Work On

• Optimizeand deploy high ‑ high-performanceLLM inference pipelines

• Own inference runtimes across data center, edge, and embedded platforms

• Push model performance through quantization, kernel fusion, and cache optimization

• Drive latency and throughput improvements that directlyimpactproduction products

• Enable efficient, reliable deployment without external vendor dependency

Core Responsibilities

Inference Engines & Runtime

• Build deepexpertiseand ownership of:

• vLLM

• TensorRT‑LLM

• llama.cpp

• QAIRT

• Extend and tune inference engines using custom CUDA kernels

• Adapt runtimes for constrained and embedded deployment environments

Quantization & NumericalOptimisation

• Implement and evaluatequantisationstrategies:

• INT8, INT4, FP4, FP8, mixed precision

• AWQ

• GPTQ

• Balance accuracy, latency, memory footprint, and throughput

KV Cache Optimization

• Optimizekey–value cache performance through:

• Paging

• Prefix caching

• Cache‑awarememory layout design

• Reduce memory pressure while sustaining high throughput

Latency & Throughput Optimisation

• Design and tune:

• Batching strategies

• Continuous batching

• Speculative decoding

• Optimize tail latency and tokens/sec under real production traffic patterns

What Success Looks Like

• Models deploy efficiently on edge and embedded devices, not just servers

• Tokens/sec significantly outperform baseline implementations

• End‑to‑endlatency is minimized and predictable

• Inference cost per request is materially reduced

• The company is no longer dependent on partners for inference optimization

Required Experience & Skills

Strongly Required

• Proven experienceoptimizing ML inference performance in production

• Deep understanding of GPU architecture and memory hierarchies

• Hands‑onexperience with CUDA and low‑level performance tuning

• Experience deploying models beyond research environments

Critical Technical Skills

• Inference engines:vLLM,TensorRT‑LLM, llama.cpp, QAIRT

• CUDA kernel development and profiling

• Quantisationtechniques: INT8/INT4/FP4/FP8, AWQ, GPTQ

• KV cacheoptimisationand memory layout design

• Latencyoptimisation: batching, speculative decoding, continuous batching

Common ProblemsYou’ll Be Solving

• Deploy efficiently on edge or embedded targets

• Achieve competitive tokens/sec

• Reduce and stabilize inference latency

You will be responsible for closing these gaps, creating a major competitive advantage.

What we offer

We offer a generous compensation and benefits package (in addition to the base salary), including:

• Salary range$185,000.00 USD - $280,000.00 USD It is not typical for offers to be made at or near the top of the range. The actual salary will be determined based on experience and other job-related factors.

• Annual bonus opportunity

• Insurance coverage (medical, dental, vision, life, and disability)

• Paid time off

• Paid holidays

• Company contribution to the RRSP (Registered Retirement Savings Plan)

• Equity awards for certain positions and levels

• Remote and/or hybrid work available depending on the position

All compensation and benefits are subject to the terms and conditions of the underlying plans or programs, as applicable, and may be amended, terminated, or replaced from time to time.

Cerence Inc. (Nasdaq: CRNC and www.cerence.com) is the global industry leader in creating unique, moving experiences for the automotive world. Spun out from Nuance in October 2019, Cerence is a new, independent company that has quickly gained traction as a leader in the automotive voice assistant space, working with all of the world’s leading automakers – from Ford and Fiat Chrysler to Daimler, Audi and BMW to Geely and SAIC – to transform how a car feels, responds and learns. Its track record is built on more than 20 years of industry experience and leadership and more than 500 million cars on the road today across more than 70 languages.

AsCerencelooks to the future and continues an ambitious growth agenda,we need someonetojointheteam and help build the future of voice and AI in cars. This is an exciting opportunity to joinCerence’spassionate, dedicated, global team and be a part of meaningful innovation in a rapidly growing industry.

EQUAL OPPORTUNITY EMPLOYER

Cerence is firmly committed to Equal Employment Opportunity (EEO) and to compliance with all federal, state and local laws that prohibit employment discrimination on the basis of age, race, color, gender, gender identity, gender expression, sex, sex stereotyping, pregnancy, national origin, ancestry, religion, physical or mental disability, medical condition, marital status, citizenship status, sexual orientation, protected military or veteran status, genetic information and other protected classifications. Cerence Equal Employment Opportunity Policy Statement.

All prospective and current Employees need to remain vigilant when it comes to executing security policies in the workplace. This includes:

• Following workplace security protocols and training programs to familiarize with the ways to maintain a safe workplace.

• Following security procedures to report any suspicious activity.

• Having respect for corporate security procedures to allow those procedures to be effective.

• Adhering to company’s compliance and regulations.

• Encouraging to follow a zero tolerance for workplace violence.

• Basic knowledge of information security and data privacy requirements (e.g., how to protect data & how to be handling this data).

• Demonstrative knowledge of information security through internal training programs.