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Founding Engineer - Machine Learning

Summary

Build and deploy production-grade ML systems from scratch for an AI data and services startup, focusing on LLMs, embeddings, and reinforcement learning pipelines.

About the Role

This is a rare opportunity to join a Series A AI data and services company as a Founding ML Engineer, working directly alongside the founding team to build and scale core machine learning systems from the ground up. You will bridge research and engineering — designing, training, and shipping production-grade models for top AI frontier labs — while helping shape the company's technical culture and infrastructure.

The company specializes in high-quality training and post-training data, reinforcement learning environments, and intelligent agents that accelerate AI model performance for both frontier labs and enterprises. This is a high-ownership, high-impact role: your work will directly establish the foundation for how the team delivers measurable ML outcomes.

Visa sponsorship is not available for this role.

What You'll Do

  • Build and optimize end-to-end ML pipelines, from data ingestion through to deployment.

  • Implement and fine-tune LLMs, embeddings, and generative models for real-world applications.

  • Develop efficient training and inference systems leveraging distributed compute.

  • Partner with data and product teams to translate ideas into measurable ML impact.

  • Contribute to model monitoring, evaluation, and continual learning frameworks.

  • Establish best practices in model versioning, reproducibility, and scalability.

What We're Looking For

Required:

  • 3–10 years of experience as an ML Engineer, Applied Scientist, or Research Engineer.

  • Proficiency in Python and at least one major ML framework: PyTorch, TensorFlow, or JAX.

  • Strong grasp of ML fundamentals — data preprocessing, feature engineering, model training, and optimization.

  • Hands-on experience with distributed systems, cloud ML infrastructure (AWS, GCP, or Azure), and MLOps tooling such as Weights & Biases or MLflow.

  • Comfort working with large datasets and high-throughput systems.

  • Strong bias for action, ability to work autonomously, and genuine eagerness to build something from scratch.

Compensation & Benefits

  • Salary range: $220,000 – $300,000 USD annually

  • Early-stage equity commensurate with a founding team role

  • Opportunity to define technical culture and ML infrastructure at the ground level

Location

  • On-site in Mountain View, California, United States

  • Remote work is not available for this position

What this application asks

ashby

Name, Email, Resume

  • LinkedIn optional
  • Do you have work authorization to work in that country? yes / no

See also

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