A Moving Experience.
We are looking for a scientist to be a part of XUI- Cerence's conversational AI platform using large language models including multimodal (both on Edge and in cloud).

What You Will Work On
- Design and train large‑scale transformer and hybrid foundation models
- 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 solid 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

Optimization Dynamics & Training Stability
- Own optimizer and scheduler choices, including:
- AdamW
- Lion
- Adafactor
- Learning‑rate and warmup schedulers
- Understand and debug:
- Optimizer instability
- Gradient pathologies





Scaling Laws & Compute Tradeoffs
- Apply and validate scaling laws
- Navigate Chinchilla‑style compute vs data tradeoffs

Loss Functions & Alignment
- Design and experiment with loss functions including:
- Next‑token prediction
- Contrastive objectives
- RLHF, DPO, GRPO

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

Cerence Inc. (Nasdaq: CRNC and www.cerence.com) is the global industry leader in creating unique, moving experiences for the automotive world. Spun out from Nuance in October 2019, Cerence is a new, independent company that has quickly gained traction as a leader in the automotive voice assistant space, working with all of the world’s leading automakers – from Ford and Fi

📌 Senior AI scientist (India)
🏢 Cerence
📍 India