About the RoleThis is a founding-team ML engineering role at an early-stage AI data and services company, where you will build and scale core machine learning systems from the ground up. You will bridge research and production engineering, shipping models that directly serve frontier AI labs and enterprises, while shaping the technical culture and infrastructure of a fast-growing team.
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 to 10 years of experience as an ML Engineer, Applied Scientist, or Research Engineer, with seniority appropriate for a founding-engineer role.
- Proficiency in Python and at least one major ML framework: PyTorch, TensorFlow, or JAX.
- Strong grasp of ML fundamentals, including 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 and Biases or MLflow.
- Comfort working with large datasets and high-throughput systems.
- Ability to debug ML systems independently, without relying on AI coding assistants.
- Background at an AI data, labeling, or frontier model company is a strong plus.
- Bias for action, autonomy, and genuine excitement about building something from scratch.
Compensation & BenefitsBase salary:
$220,000 to $300,000 USD annually, plus equity. Visa sponsorship is not available for this role.
LocationOn-site in
Mountain View, California, United States.
