Cerence
From Cerence's careers page
Status: listed on the employer's own careers page when we last checked (daily). Confirm on their site before applying.
We’re looking for an exceptional Senior Principal AI Scientist in Generative AI who is ready to drive the future of mobility with us!
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!
Design and train large‑scale transformer and hybrid foundation models
Own model architecture choices across text, multimodal, and emerging paradigms
Diagnose and resolve training instabilities at scale
Navigate scaling tradeoffs across data, compute, and architecture
Define the technical direction for next‑generation models
Core Responsibilities
Deep Learning & Transformer Foundations
Apply strong fundamentals in deep learning and representation learning
Attention variants
RoPE, ALiBi
Grouped Query Attention (GQA)
Mixture‑of‑Experts (MoE)
Build models from first principles, not just adapt pre‑existing codebases
Optimisation Dynamics & Training Stability
AdamW
Lion
Adafactor
Learning‑rate and warmup schedulers
Optimizer instability
Gradient pathologies
Divergence at large scale
Scaling Laws & Compute Tradeoffs
Apply and validate scaling laws
Navigate Chinchilla‑style compute vs data tradeoffs
Make informed decisions about model size, dataset size, and training duration
Loss Functions & Alignment
Next‑token prediction
Contrastive objectives
RLHF, DPO, GRPO
Understand how loss design impacts convergence, generalization, and alignment
Distributed Foundation Model Training
FSDP
ZeRO‑3
Tensor parallelism
Pipeline parallelism
Apply
Mixed precision (bf16, fp8)
Gradient checkpointing
Partner closely with ML systems teams while retaining architectural ownership
Architecture Innovation
MoE routing strategies
Multimodal fusion architectures
SSM / hybrid architectures
Design architectures with KV cache efficiency and inference implications in mind
What Success Looks Like
Training remains stable as models scale in size and complexity
Architectural decisions are principled and defensible
Models converge faster and generalize better due to architecture and optimisation choices
Failure modes are understood, not mysterious
The organization develops true in‑house foundation model expertise
Required Experience & Skills
Strongly Required
Deep theoretical and practical understanding of modern deep learning
Hands‑on experience training large models from scratch
Ability to reason about optimization, not just tune hyperparameters
Comfort operating in ambiguous, research‑driven environments
Critical Technical Skills
Transformer internals and attention mechanisms
Optimisation algorithms and training dynamics
Scaling laws and compute/data tradeoffs
Distributed training strategies and mixed precision
Architecture innovation for large, real‑world models
Common Problems You’ll Be Solving
How optimizer dynamics interact with architecture
When scaling laws break down
The real tradeoffs between data, compute, and model design
We offer a generous compensation and benefits package (in addition to the base salary), including:
Salary range $185,000.00 - $280,000.00 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.
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