Greylock Partners
Founding Machine Learning Engineer
Greylock Partners, Redwood City, California, United States, 94061
Early-stage, cybersecurity investment (valued over $100M at Seed), founded by a successful serial entrepreneur, is looking to hire a Founding MLE with a strong background standing up training/inference pipelines in the cloud (GCP/AWS), good MLOps familiarity, and full end‑to‑end ownership.
Summary Help architect the core ML pipelines and codebase for an industry‑first solution in security. Our ideal candidate will be able to take research‑grade ML experiments and ensure they can be run in a stable and repeatable manner in the enterprise.
Key Qualifications
Advanced degree in CS (or a related field) with 4+ years industry experience and 1+ years working with LLMs in a large‑scale production environment
Prior experience with a zero‑to‑one
Proven ability to collaborate across different teams and adapt to new fields
Design and train machine learning models from scratch, developing and testing new architectures on large, complex datasets
Build and optimize end‑to‑end training pipelines, including data preprocessing, distributed training, and experiment tracking
Tune and evaluate model performance through systematic experimentation, hyperparameter optimization, and metric analysis
Collaborate with data and infrastructure teams to manage compute resources, ensure reproducibility, and prepare models for deployment
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Summary Help architect the core ML pipelines and codebase for an industry‑first solution in security. Our ideal candidate will be able to take research‑grade ML experiments and ensure they can be run in a stable and repeatable manner in the enterprise.
Key Qualifications
Advanced degree in CS (or a related field) with 4+ years industry experience and 1+ years working with LLMs in a large‑scale production environment
Prior experience with a zero‑to‑one
Proven ability to collaborate across different teams and adapt to new fields
Design and train machine learning models from scratch, developing and testing new architectures on large, complex datasets
Build and optimize end‑to‑end training pipelines, including data preprocessing, distributed training, and experiment tracking
Tune and evaluate model performance through systematic experimentation, hyperparameter optimization, and metric analysis
Collaborate with data and infrastructure teams to manage compute resources, ensure reproducibility, and prepare models for deployment
#J-18808-Ljbffr