Member of Technical Staff - ML Research

Kindredventures; San Francisco (CA); On-site; USD 120,000 - 160,000; Full time

Job summary

Kindredventures in San Francisco seeks a Machine Learning Engineer to work with the full ML stack, implementing advanced model architectures and building extensive data pipelines for large datasets.

The ideal candidate will have expertise in machine learning principles, extensive experience in training models, and familiarity with distributed training processes. A bonus is knowledge in meteorology or numerical simulations.

Qualifications

• Strong grasp of machine learning fundamentals and depth in at least one core domain.
• Experienced at training models and understanding experiment results through careful analysis.
• Experienced at writing and optimizing massive petabyte-scale data pipelines.
• Familiarity with distributed training is a must.
• Familiarity with meteorology, computational fluid dynamics, and/or numerical simulations is a bonus.

Responsibilities

• Work across the full ML stack including data, model, eval, and infrastructure.
• Implement novel model architectures and training algorithms.
• Build data pipelines and training infrastructure for petabyte-scale datasets.
• Rapidly iterate on experiments and ablations.
• Stay up-to-date on research to bring new ideas to work.

Skills

Machine learning fundamentals

Data pipeline optimization

Model training

Ablation studies

Distributed training

Familiarity with meteorology

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.