Sr. Principal Software Scientist 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 AI Scientist in Generative AI who is ready to drive the future of mobility with us! What You Will Work On 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 Design and modify transformer architectures, including: Attention variants RoPE ,  ALiBi Grouped Query Attention (GQA) Mixture ‑ of ‑ Experts ( MoE ) Build models  from first principles , not just adapt  pre ‑ existing codebases Optimi s ation Dynamics & Training Stability Own optimi z er and scheduler choices, including: AdamW Lion Adafactor Learning ‑ rate and warmup schedulers Understand and debug: 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 Design and experiment with loss functions including: Next ‑ token prediction Contrastive objective s RLHF ,  DPO ,  GRPO Understand how loss design impacts convergence, generalization, and alignment Distributed Foundation Model Training Design and execute large ‑ scale training using: 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 Explore and implement novel model designs, including: 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 optimi s ation 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 Optimi s ation 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 Why training diverges at scale How optimizer dynamics interact with architecture When scaling laws break down The real tradeoffs between data, compute, and model design What we offer 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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Must-haves

A Moving Experience. Who is Cerence AI?
Our Driving Force
What You Will Work On
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
Deep Learning & Transformer Foundations
Apply strong fundamentals in deep learning and representation learning
Design and modify transformer architectures, including:
Attention variants
RoPE ,  ALiBi
Grouped Query Attention (GQA)
Mixture ‑ of ‑ Experts ( MoE )
Build models  from first principles , not just adapt  pre ‑ existing codebases
Optimi s ation Dynamics & Training Stability
Own optimi z er and scheduler choices, including:
Learning ‑ rate and warmup schedulers
Optimizer instability
Gradient pathologies
Divergence at large scale
Scaling Laws & Compute Tradeoffs
Apply and validate  scaling laws

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

aiml

Platform: workday
Effort: long

Benefits

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Dental

Vision

Paid Time Off