Sr. Principal Software Scientist

Cerence; United States; Remote; USD 180,000 - 260,000; Full time

Job summary

Cerence is seeking a Senior Principal AI Scientist in Generative AI to lead development of large-scale transformer models for mobility-conscious AI experiences. You will own architecture and training strategies, addressing training stability and scaling challenges across data and compute, while shaping the direction of next-generation models at Cerence AI.

The role emphasizes deep learning foundations, transformer innovations, and advanced loss/feedback mechanisms to align models with real-world

Qualifications

• Apply strong fundamentals in deep learning and representation learning.
• Design and modify transformer architectures, including attention variants (RoPE, ALiBi, GQA).
• Own optimizer and scheduler choices, including AdamW, Lion, Adafactor, and learning-rate warmups.

Responsibilities

• Design and train large-scale transformer models.
• Own model architecture choices across text, multimodal, and emerging paradigms.
• Diagnose training instabilities at scale and optimize compute efficiency.
• Navigate scaling tradeoffs between data, compute, and architecture.
• Define the technical direction for next-generation models.

Skills

Deep learning

Transformer architectures

Model training & optimization

Attention mechanisms

Tools

AdamW

Lion

Adafactor

MoE (Mixture of Experts)

RoPE / ALiBi

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 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 ‑ scaletransformer 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 fornext‑generationmodels

Core Responsibilities

Deep Learning & Transformer Foundations

• Apply strong fundamentals in deep learning and representation learning

• Design andmodifytransformer architectures, including:

• Attention variants

• RoPE , ALiBi

• Grouped Query Attention (GQA)

• Mixture ‑ of ‑Experts(MoE)

• Build models from first principles , not just adaptpre‑existingcodebases

OptimisationDynamics & Training Stability

• Own optimizer and scheduler choices, including:

• AdamW

• Lion

• Adafactor

• Learning‑rateand warmup schedulers

• Understand and debug:

• Optimizer instability

• Gradient pathologies

• Divergence at large scale

Scaling Laws & Compute Tradeoffs

• Apply andvalidate scaling laws

• Navigate Chinchilla ‑ stylecompute 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 ‑ tokenprediction

• Contrastive objectives

• RLHF , DPO , GRPO

• Understand how loss design impacts convergence, generalization, and alignment