Founding Machine Learning Engineer

About the Role This is a founding-level Machine Learning Engineer role at a well-funded Series A AI data and services startup based in Mountain View, CA.

You will join a small, high-caliber team building and scaling core ML systems from the ground up — bridging research and engineering to design, train, and ship production-grade models for top AI frontier labs.

This is a high-ownership position where your work directly shapes the company's technical culture, infrastructure, and long-term ML impact.

The company specializes in high-quality training and post-training data, reinforcement learning environments, and intelligent agents — serving both frontier AI labs and enterprise customers. 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 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.

Strong bias for action, ability to work autonomously, and genuine excitement about building from scratch. Compensation & Benefits Base salary: $220,000 – $300,000 USD annually Equity participation