Sr. Principal Software Scientist

Cerence

43d•$185,000 - $280,000•Hybrid

About The Position

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 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. We’re looking for an exceptional Senior Principal AI Scientist in Generative AI who is ready to drive the future of mobility with us!

Requirements

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
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
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.

Responsibilities

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
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
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
Apply and validate scaling laws
Navigate Chinchilla‑style compute vs data tradeoffs
Make informed decisions about model size, dataset size, and training duration
Design and experiment with loss functions including: Next‑token prediction, Contrastive objectives, RLHF, DPO, GRPO
Understand how loss design impacts convergence, generalization, and alignment
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
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