Perplexity
Member of Technical Staff (AI Infrastructure Engineer)
Part remote, part office, you need to live within commuting distance of a named location.
Hybrid · London
Employer listed it 5 months ago · Added 3 days ago
Been open since 5 months ago. Long-running listings are sometimes left up after the role is filled.
Salary
Not stated
Location
Hybrid · London
Timezone
Not stated
Contract
Full-time
Experience
Lead
Category
Software
This employer didn't state pay. Jobs like this usually pay around $200k–$275k a year, a typical range taken from 597 lead-level software roles on Nomaders that do state pay. It's a guide, not an offer.
Remote flexibility
Hybrid
This role is only partly remote, the employer expects time in the office around London, Hybrid, so you need to live within commuting distance.
What the employer says
- Source listing states candidate location: "London, Hybrid"
- Listing mentions "Hybrid"
What Nomaders makes of it
- Not suitable if you plan to move between countries
The quotes above are the employer's own words; the reading is ours. Always check the original listing and employment terms before working from another country.
About the role
We are looking for an AI Infra engineer to join our growing team. We work with Kubernetes, Slurm, Python, C++, PyTorch, and primarily on AWS. As an AI Infrastructure Engineer, you will be partnering closely with our Inference and Research teams to build, deploy, and optimize our large-scale AI training and inference clusters.
Responsibilities
Design, deploy, and maintain scalable Kubernetes clusters for AI model inference and training workloads
Manage and optimize Slurm-based HPC environments for distributed training of large language models
Develop robust APIs and orchestration systems for both training pipelines and inference services
Implement resource scheduling and job management systems across heterogeneous compute environments
Benchmark system performance, diagnose bottlenecks, and implement improvements across both training and inference infrastructure
Build monitoring, alerting, and observability solutions tailored to ML workloads running on Kubernetes and Slurm
Respond swiftly to system outages and collaborate across teams to maintain high uptime for critical training runs and inference services
Optimize cluster utilization and implement autoscaling strategies for dynamic workload demands
Qualifications
Strong expertise in Kubernetes administration, including custom resource definitions, operators, and cluster management
Hands-on experience with Slurm workload management, including job scheduling, resource allocation, and cluster optimization
Experience with deploying and managing distributed training systems at scale
Deep understanding of container orchestration and distributed systems architecture
High level familiarity with LLM architecture and training processes (Multi-Head Attention, Multi/Grouped-Query, distributed training strategies)
Experience managing GPU clusters and optimizing compute resource utilization
Required Skills
Expert-level Kubernetes administration and YAML configuration management
Proficiency with Slurm job scheduling, resource management, and cluster configuration
Python and C++ programming with focus on systems and infrastructure automation
Hands-on experience with ML frameworks such as PyTorch in distributed training contexts
Strong understanding of networking, storage, and compute resource management for ML workloads
Experience developing APIs and managing distributed systems for both batch and real-time workloads
Requirements
- ·Strong expertise in Kubernetes administration, including custom resource definitions, operators, and cluster management
- ·Hands-on experience with Slurm workload management, including job scheduling, resource allocation, and cluster optimization
- ·Experience with deploying and managing distributed training systems at scale
- ·Deep understanding of container orchestration and distributed systems architecture
- ·High level familiarity with LLM architecture and training processes (Multi-Head Attention, Multi/Grouped-Query, distributed training strategies)
Benefits
No benefits package published with this listing. Ask about it at first interview.
How to apply
- 1Check the flexibility label above, hybrid, matches where you plan to live and work.
- 2Tailor your CV to the role at Perplexity, mentioning your remote working experience.
- 3Apply directly on the employer's careers page using the button below. Nomaders never handles your application.
Found 4d ago. Last checked 24 Sept. Always confirm the details on the original posting, salary and location can change after publication.
Listing sourced from Company boards.
Similar roles
Other open software roles with comparable remote rules.
Typically $200k to $275k per year · Free accounts get one application link on us.