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

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Design and train  large ‑ scale transformer and hybrid foundation models

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Own model architecture choices across text, multimodal, and emerging paradigms

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Diagnose and resolve  training instabilities at scale

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Navigate scaling tradeoffs across data, compute, and architecture

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Define the technical direction for next‑generation models

Core Responsibilities

Deep Learning & Transformer Foundations

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Apply strong fundamentals in deep learning and representation learning

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Design and modify transformer architectures, including:

- Attention variants

- RoPE,  ALiBi

- Grouped Query Attention (GQA)

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Mixture ‑ of ‑ Experts (MoE)

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Build models  from first principles , not just adapt pre‑existing codebases

OptimisationDynamics & Training Stability

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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

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Apply and validate  scaling laws

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Navigate  Chinchilla ‑ style compute vs data tradeoffs

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Make informed decisions about model size, dataset size, and training duration

Loss Functions & Alignment

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Design and experiment with loss functions including:

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Next ‑ token prediction

- Contrastive objectives

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RLHF ,  DPO ,  GRPO

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Understand how loss design impacts convergence, generalization, and alignment

Distributed Foundation Model Training

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Design and execute large‑scale training using:

- FSDP

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ZeRO ‑ 3

- Tensor parallelism

- Pipeline parallelism

- Apply

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Mixed precision ( bf16 ,  fp8 )

- Gradient checkpointing

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Partner closely with ML systems teams while retaining architectural ownership

Architecture Innovation

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Explore and implement novel model designs, including:

- MoE routing strategies

- Multimodal fusion architectures

- SSM / hybrid architectures

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Design architectures with KV cache efficiency and inference implications in mind

What Success Looks Like

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Training remains stable as models scale in size and complexity

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Architectural decisions are principled and defensible

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Models converge faster and generalize better due to architecture and optimisation choices

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Failure modes are understood, not mysterious

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The organization develops true  in ‑ house foundation model expertise

Required Experience & Skills

Strongly Required

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Deep theoretical and practical understandi