Founding Machine Learning Engineer

Greylock Partners; Redwood City (CA); On-site; USD 150,000 - 200,000; Full time

Job summary

A cybersecurity investment firm in California is seeking a Founding Machine Learning Engineer to architect core ML pipelines and improve enterprise security solutions. The ideal candidate has an advanced degree in computer science, 4+ years of industry experience, and a proven ability to create ML models. You will work on optimizing training pipelines and ensuring reproducibility in a large-scale environment. This role offers the opportunity to collaborate across teams and contribute to a cutting-edge solution.

Qualifications

• 4+ years of industry experience in ML.
• 1+ years working with LLMs in production.
• Experience with zero-to-one projects.

Responsibilities

• Architect the core ML pipelines for an innovative security solution.
• Transform research-grade ML experiments into stable, deployable Solutions.
• Optimize end-to-end training pipelines and manage compute resources.

Skills

Collaboration across teams

Model performance tuning

Data preprocessing

Building ML pipelines

Education

Advanced degree in CS or related field

Job description

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