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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 ) 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.
As Cerence looks to the future and continues an ambitious growth agenda, we need someone to join the team and help build the future of voice and AI in cars. This is an exciting opportunity to join Cerence’s passionate, 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.
Originally posted on Himalayas
Who can apply
Eligible countries: United States. Accepted UTC offsets: UTC-10, UTC-9, UTC-8, UTC-7, UTC-6, UTC-5, UTC+14. Review the full description for employer-specific work authorization, residency and schedule requirements.