Senior Principal AI Engineer

Cerence; United States; Remote; USD 180,000 - 260,000; Full time

Job summary

Cerence is seeking an experienced ML Systems Engineer to design and operate distributed training infrastructure for large neural networks across GPU clusters. You will optimize multi-node, multi-GPU execution and diagnose bottlenecks in compute, memory, and network to maximize throughput and utilization.

Collaborate with research and applied ML teams to productionize large-model training pipelines, improve stability, and implement advanced memory optimizations such as activation checkpointing

Qualifications

• 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).
• Low-level understanding of GPU communication and networking.
• 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.

Responsibilities

• Distributed Training Infrastructure: Build and optimize GPU cluster orchestration.
• Ensure efficient scheduling, isolation, and fairness across training workloads.
• Communication & Networking optimization.
• Training frameworks: scale large-model training and manage multi-node launches.
• Memory and performance optimization across compute and data paths.
• Collaborate with research and applied ML teams to productionize pipelines.

Skills

Distributed systems

ML systems

GPU clusters

PyTorch Distributed

Megatron-LM

DeepSpeed

NCCL

RDMA

InfiniBand

NVLink

Slurm

Kubernetes

Ray

RunAI

Activation checkpointing

ZeRO

Tools

Slurm

Kubernetes

Ray

RunAI

NCCL

RDMA

InfiniBand

NVLink

PyTorch Distributed

Megatron-LM

DeepSpeed

Job description

A Moving Experience.

What You Will Work On

• Design andoperatedistributed training systems for large neural networks(autoregressive, diffusion,State Space Modelsetc.)across GPU clusters

• Optimisemulti‑node, multi‑GPU execution tomaximizethroughput andutilization

• Diagnose&resolve bottlenecks across compute, memory, and network

• Improve training stability and fault tolerance at scale

• Partner with research and applied ML teams to productionizelarge‑modeltraining pipelines

Core Responsibilities

• Distributed Training Infrastructure

• Build andoptimizeGPU cluster orchestration using:

• Slurm

• Kubernetes

• Ray

• RunAI

• Ensure efficient scheduling, isolation, and fairness across training workloads

• Communication & Networking

• Optimizeand debug distributed communication using:

• NCCL

• RDMA

• InfiniBand

• NVLink

• Minimizenetworking bottlenecks that dominateend‑to‑endtraining time

• Training Frameworks

• Scale large-model training using:

• PyTorchDistributed

• Megatron‑LM

• DeepSpeed

• Ownmulti‑nodelaunch 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

• GPUutilizationconsistently 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

• Deephands‑onexperience with distributed systems or ML systems

• Experience runninglarge‑scaleworkloads on GPU clusters

• Production experience withPyTorchdistributed training

• Strong understanding of parallelism strategies (data, tensor, pipeline parallelism)

• Low‑levelunderstanding of GPU communication and networking

• Critical Technical Skills

• GPU orchestration:Slurm, Kubernetes, Ray,RunAI

• Communication libraries: NCCL, RDMA, InfiniBand,NVLink

• Training frameworks:PyTorchDistributed,Megatron‑LM,DeepSpeed

• Memoryoptimisation: activation checkpointing,ZeROoffload techniques

Common ProblemsYou’llBe Solving

• Many teams fail at scale because:

• GPUutilizationis low despite large clusters

• Networking and communication dominate training time

• Training jobs crash or become unstable at large scale

• You will be explicitly focused oneliminatingthese 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 systemsexpertiseis valued over pure model architecture experience.

Why This Role Matters

Without robust distributed training infrastructure, progress on largemodelsstalls. This role directly enables:

• Larger models

• Faster iteration cycles

• More reliable research-to-production pipelines

You will be building the foundation that makeslarge‑scaleAI possible.

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 Fiat Chrysler to Daimler, Audi and BMW to Geely and SAIC – to transform how a car feels, responds and learns. Its track record is built on more than 20 years of industry experience and leadership and more than 500 million cars on the road today across more than 70 languages.

AsCerencelooks to the future and continues an ambitious growth agenda,we need someonetojointheteam and help build the future of voice and AI in cars. This is an exciting opportunity to joinCerence’spassionate, dedicated, global team and be a part of meaningful innovation in a rapidly growing industry.

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. Cerence Equal Employment Opportunity Policy Statement.

All prospective and current Employees need to remain vigilant when it comes to executing security policies in the workplace. This includes:

• Following workplace security protocols and training programs to familiarize with the ways to maintain a safe workplace.

• Following security procedures to report any suspicious activity.

• Having respect for corporate security procedures to allow those procedures to be effective.

• Adhering to company’s compliance and regulations.

• Encouraging to follow a zero tolerance for workplace violence.

• 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.