Senior AI Scientist

Cerence Inc.

Senior AI Scientist designing and training large scale foundation models at Cerence, a leader in automotive voice AI solutions. Involves deep learning, model architecture, and large-scale training.

Posted 7/6/2026full-timeRemote • California • 🇺🇸 United StatesSenior💰 $123,500 - $197,600 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and training large-scale transformer and hybrid foundation models, with a strong foundation in deep learning and representation learning. Proficient in optimizing model architectures and training processes, including advanced techniques such as mixed precision and gradient checkpointing.

Highest-signal resume keywords

Large Scale Transformer Model DesignDeep Learning FundamentalsOptimizer and Scheduler ChoicesLoss Function ExperimentationLarge-Scale Training Execution

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Transformer Architecture DesignRepresentation LearningModel OptimizationGradient CheckpointingMixed Precision (bf16, fp8)Next-Token PredictionContrastive ObjectivesScaling Laws ValidationFSDPZeRO-3

Tools & Technologies

AdamWLionAdafactorTensor ParallelismPipeline Parallelism

Industry Keywords

Multimodal ModelsTraining InstabilitiesScaling TradeoffsEmerging ParadigmsAttention Variants

About the role

Key responsibilities & impact

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 and compute
Apply strong fundamentals in deep learning and representation learning
Build models from first principles, not just adapt pre-existing codebases
Own optimiser and scheduler choices, including AdamW, Lion, Adafactor
Design and experiment with loss functions including next-token prediction and contrastive objectives
Design and execute large-scale training using FSDP, ZeRO-3, Tensor parallelism, Pipeline parallelism
Apply mixed precision (bf16, fp8) and gradient checkpointing

Requirements

What you’ll need

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 and compute
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
Own optimiser and scheduler choices, including AdamW, Lion, Adafactor
Understand and debug optimiser instability and gradient pathologies
Apply and validate scaling laws
Design and experiment with loss functions including next-token prediction and contrastive objectives
Design and execute large-scale training using FSDP, ZeRO-3, Tensor parallelism, Pipeline parallelism
Apply mixed precision (bf16, fp8) and gradient checkpointing
Partner closely with ML systems teams

Benefits

Comp & perks

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