Sr. Principal Software Engineer at Cerence Inc

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 Senior Principal Software Engineer who is ready to drive the future of mobility with us! Job Description: What You Will Work On Optimize and deploy  high ‑ performance LLM 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 directly impact production products Enable efficient, reliable deployment without external vendor dependency Core Responsibilities Inference Engines & Runtime Build deep expertise and 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 & Numerical Optimi s ation Implement and evaluate quanti s ation strategies: INT8, INT4, FP4, FP8, mixed precision AWQ GPTQ Balance accuracy, latency, memory footprint, and throughput KV Cache Optimization Optimize key–value cache performance through: Paging Prefix caching Cache ‑ aware  memory layout design Reduce memory pressure while sustaining high throughput Latency & Throughput Optimi s ation 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 ‑ end latency 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 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 Critical Technical Skills Inference engines:  vLLM ,  TensorRT ‑ LLM, llama.cpp, QAIRT CUDA kernel development and profiling Quanti s ation techniques: INT8/INT4/FP4/FP8, AWQ, GPTQ KV cache optimi s ation and memory layout design Latency optimi s ation : batching, speculative decoding, continuous batching Common Problems You’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. As Cerence looks to the future and continues an ambitious growth agenda, we need someone to join the team and help build the future of voice and AI in cars. This is an exciting opportunity to join Cerence’s passionate, 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.

Requirements

Must-haves

A Moving Experience. Who is Cerence AI?
Our Driving Force
What You Will Work On
Optimize and deploy  high ‑ performance LLM 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 directly impact production products
Enable efficient, reliable deployment without external vendor dependency
Inference Engines & Runtime
TensorRT‑LLM
Extend and tune inference engines using  custom CUDA kernels
Adapt runtimes for constrained and embedded deployment environments
Quantization & Numerical Optimi s ation
Implement and evaluate quanti s ation strategies:
INT8, INT4, FP4, FP8, mixed precision
Balance accuracy, latency, memory footprint, and throughput
KV Cache Optimization
Prefix caching
Cache ‑ aware  memory layout design
Reduce memory pressure while sustaining high throughput
Latency & Throughput Optimi s ation
Batching strategies
Continuous batching
Speculative decoding

Nice-to-haves

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Application prep

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Keywords to mirror

aillmml

Platform: workday
Effort: long

Benefits

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Dental

Vision

Paid Time Off