Sr. Principal Software Scientist

About the role

Cerence AI is seeking a Senior Principal AI Scientist in Generative AI to drive the future of mobility. The ideal candidate will have a strong theoretical and practical understanding of modern deep learning, hands-on experience training large models, and a comfort level operating in ambiguous, research-driven environments.

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
• 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
• Optimise dynamics and 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

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, Optimisation 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 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

What We Offer

• Salary range: $185,000.00 - $280,000.00
• 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

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.