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Member of Technical Staff - ML Research

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 Research. Causal Labs is seeking a Member of Technical Staff for ML Research. This role involves working across the full ML stack, implementing novel model architectures and training algorithms, and building data pipelines for massive, multimodal datasets. The ideal candidate will rapidly iterate on experiments and stay updated on research to bring new ideas to work.

Key focus areas include Work across the full ML stack (data, model, eval, and infrastructure), Implement novel model architectures and training algorithms, and Build data pipelines and training infrastructure for massive, petabyte-scale, multimodal datasets.

Preferred (not required): Machine Learning Fundamentals, Computer Vision, Sensor Fusion, and Language Models.

Skills & qualifications

RequiredNice to have

Skills

Machine Learning FundamentalsComputer VisionSensor FusionLanguage ModelsPhysics-Informed NNsTraining ModelsExperiment Results AnalysisAblation StudiesWriting Data PipelinesOptimizing Data PipelinesDistributed TrainingMeteorologyComputational Fluid DynamicsNumerical SimulationsProblem-SolvingRapid ExecutionQuick Learning

Qualifications

Experienced at Training ModelsExperienced at Writing and Optimizing Massive Petabyte-Scale Data Pipelines

Full job description

Responsibilities

  • Work across the full ML stack (data, model, eval, and infrastructure)

  • Implement novel model architectures and training algorithms

  • Build data pipelines and training infrastructure for massive, petabyte-scale, multimodal datasets

  • Rapidly iterate on experiments and ablations

  • 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 machine learning fundamentals, and depth in at least one core domain (e.g. Computer Vision, Sensor Fusion, Language Models, Physics-informed NNs)

  • Experienced at training models and understanding experiment results through careful analysis and ablation studies.

  • Experienced at writing and optimizing massive petabyte-scale data pipelines.

  • Familiarity with distributed training.

  • [bonus] Familiarity with meteorology, computational fluid dynamics, and/or numerical simulations.

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

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