Among computer and information research scientists, AI is already used for a large share of the tasks. Measured exposure for computer and information research scientists: 34% (high).

Share of this job’s tasks that AI is already used for at work, from real usage. It is not a forecast of job loss, and a low number means no use seen yet. Source: Anthropic Economic Index.

Check how this applies to your own job

Skills this job asks for

Say which you have and which you want to build. We use it to match you to jobs like this one.

About the job

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.

For more information, visit www.cerence.ai .

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.