About The Role

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.