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 AI Scientist in Generative AI 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 Optimisation
Implement and evaluate quantisation 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 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‑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
Quantisation techniques: INT8/INT4/FP4/FP8, AWQ, GPTQ
KV cache optimisation and memory layout design
Latency optimisation: 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 $141,400 USD - $226,300 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.