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Member of Technical Staff - ML Infra

Causal Labs

San Francisco, CAFull-timePosted 1y agoStill listed 2 days ago

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At a glance

Compensation
No compensation found
Location
San Francisco, CA
Schedule
Full-time
Work Authorization
Not specified

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Job overview

Causal Labs is hiring a Member of Technical Staff - ML Infra. Causal Labs is seeking a Member of Technical Staff for ML Infrastructure. This role involves designing, deploying, and maintaining large distributed ML training and inference clusters. The ideal candidate will develop efficient, scalable end-to-end pipelines for petabyte-scale datasets and model training, research various training approaches, and optimize GPU operations. They should possess a relentless approach to problem-solving and a rapid execution ability.

Key focus areas include Design, deploy, and maintain large distributed ML training and inference clusters, Develop efficient, scalable end-to-end pipelines to manage petabyte-scale datasets and model training, and Research and test various training approaches including parallelization techniques.

Preferred (not required): Distributed ML Training, Distributed ML Inference, ML Lifecycle Management, and Parallelization Techniques.

Skills & qualifications

RequiredNice to have

Skills

Distributed ML TrainingDistributed ML InferenceML Lifecycle ManagementParallelization TechniquesNumerical Precision Trade-OffsGPU Operations OptimizationProblem-SolvingRapid ExecutionQuick LearningOptimizing Training WorkloadsOptimizing Inference WorkloadsFSDPDeepSpeedGCPAWSAzureKubernetesDockerDistributed Task Management SystemsScalable Model Serving ArchitecturesScalable Model Deployment ArchitecturesMonitoring Best Practices for ML Systems

Full job description

Responsibilities

  • Design, deploy, and maintain large distributed ML training and inference clusters

  • Develop efficient, scalable end-to-end pipelines to manage petabyte-scale datasets and model training throughout the entire ML lifecycle

  • Research and test various training approaches including parallelization techniques and numerical precision trade-offs across different model scales

  • Analyze, profile and debug low-level GPU operations to optimize performance

  • Stay up-to-date on research to bring new ideas to work

What we’re looking for

We value a relentless approach to problem-solving, rapid execution, and the ability to quickly learn in unfamiliar domains.

  • Strong grasp of state-of-the-art techniques for optimizing training and inference workloads

  • Demonstrated proficiency with distributed training frameworks (e.g. FSDP, DeepSpeed) to train large foundation models

  • Knowledge of cloud platforms (GCP, AWS, or Azure) and their ML/AI service offerings

  • Familiarity with containerization and orchestration frameworks (e.g., Kubernetes, Docker)

  • Background working on distributed task management systems and scalable model serving & deployment architectures

  • Understanding of monitoring, logging, observability, and version control best practices for ML systems

You don’t have to meet every single requirement above.

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