About the Role

This is a founding-level ML engineering role at an early-stage AI data and services company, building core machine learning systems from the ground up alongside a small, high-ownership team. You'll bridge research and engineering to design, train, and ship production-grade models that directly serve frontier AI labs — and you'll help shape the technical culture and infrastructure from day one.

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

• 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 and cloud ML infrastructure (AWS, GCP, or Azure).

• Familiarity with MLOps tooling such as Weights & Biases or MLflow.

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

• A bias for action, ability to work autonomously, and genuine enthusiasm for building from scratch.

Compensation & Benefits

Base salary of $220,000 – $300,000 USD annually. Visa sponsorship is not available for this role.

Location

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