Sr. Principal Software Scientist

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 SeniorPrincipalAI 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

OptimisationDynamics & Training Stability

Own optimizer 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 objectives

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 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

Optimisationalgorithms 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 ) 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.

Originally posted on Himalayas