Senior Principal AI Engineer

About the role

AI That Moves the World. Careers That Move You Forward.

At Cerence AI, you'll help transform advanced AI into trusted, real-world solutions that make complex technology - think vehicles, robots, smart manufacturing, and emerging intelligent devices - more natural, reliable, and useful. Building on decades of innovation and global deployment in automotive, we are extending our expertise into new products, markets, and intelligent systems.

As part of our global team, you'll work alongside leading innovators, researchers, engineers, and business leaders to solve meaningful challenges and turn breakthrough ideas into technology people use every day. You'll have the opportunity to make a visible impact, help shape Cerence AI's next chapter, and move your own career forward.

What You Will Work On

• Design and operate distributed training systems for large neural networks (autoregressive, diffusion, State Space Models etc.) across GPU clusters
• Optimise multi-node, multi-GPU execution to maximize throughput and utilization
• Diagnose & resolve bottlenecks across compute, memory, and network
• Improve training stability and fault tolerance at scale
• Partner with research and applied ML teams to productionize large-model training pipelines

Core Responsibilities

• Distributed Training Infrastructure
• Build and optimize GPU cluster orchestration using:
• Slurm
• Kubernetes
• Ray
• RunAI
• Ensure efficient scheduling, isolation, and fairness across training workloads
• Communication & Networking
• Optimize and debug distributed communication using:
• NCCL
• RDMA
• InfiniBand
• NVLink
• Minimize networking bottlenecks that dominate end-to-end training time
• Training Frameworks
• Scale large-model training using:
• PyTorch Distributed
• Megatron-LM
• DeepSpeed
• Own multi-node launch configurations, failure recovery, and performance tuning
• Memory & Performance Optimization
• Apply advanced memory optimization techniques:
• Activation checkpointing
• ZeRO (Stage 1-3) and offload strategies
• Balance compute, memory, and communication to push model size and batch scale

What Success Looks Like

• GPU utilization consistently stays high (>80-90%)
• Training scales cleanly from single node to dozens or hundreds of GPUs
• Communication overhead is minimized and predictable
• Large training jobs run stably for days or weeks without failure
• New models can be trained faster, larger, and more reliably than before

Required Experience & Skills

• Strongly Required
• Deep hands-on experience with distributed systems or ML systems
• Experience running large-scale workloads on GPU clusters
• Production experience with PyTorch distributed training
• Strong understanding of parallelism strategies (data, tensor, pipeline parallelism)
• Low-level understanding of GPU communication and networking
• Critical Technical Skills
• GPU orchestration: Slurm, Kubernetes, Ray, RunAI
• Communication libraries: NCCL, RDMA, InfiniBand, NVLink
• Training frameworks: PyTorch Distributed, Megatron-LM, DeepSpeed
• Memory optimisation: activation checkpointing, ZeRO offload techniques

Common Problems You'll Be Solving

• Many teams fail at scale because:
• GPU utilization is low despite large clusters
• Networking and communication dominate training time
• Training jobs crash or become unstable at large scale
• You will be explicitly focused on eliminating these failure modes.

Ideal Background

• This role is a strong fit for individuals who have worked as:
• ML Systems Engineer
• Distributed Systems Engineer
• AI Infrastructure Engineer
• HPC Engineer transitioning into ML
• Experience working with large language models or foundation models is a strong plus, but deep systems expertise is valued over pure model architecture experience.

Why This Role Matters

Without robust distributed training infrastructure, progress on large models stalls. This role directly enables:

• Larger models
• Faster iteration cycles
• More reliable research-to-production pipelines

You will be building the foundation that makes large-scale AI possible.

What we offer

We offer a generous compensation and benefits package (in addition to the base salary), including:

• Salary range $141,400.00 USD - $226,300.00 USD It is not typical for offers to be made at or near the top of the range. The actual salary will be determined based on experience and other job-related factors.
• Annual bonus opportunity
• Insurance coverage (medical, dental, vision, life, and disability)
• Paid time off
• Paid holidays
• Company contribution to the 401K
• Equity awards for certain positions and levels
• Remote and/or hybrid work available depending on the position

All compensation and benefits are subject to the terms and conditions of the underlying plans or programs, as applicable, and may be amended, terminated, or replaced from time to time.

Cerence AI (NASDAQ: CRNC) helps people and intelligent systems work together naturally and reliably. Proven at global scale in automotive, Cerence provides the trusted interaction and action layer connecting people, machines, and intelligent systems. Its AI platform enables natural interaction and dependable action across complex real-world environments where reliability, safety, security, and performance are critical. Working with customers and partners worldwide, Cerence transforms advanced AI into trusted solutions that bridge the gap between AI potential and real-world performance.