About this role

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

3–10 years of hands-on experience as an ML Engineer, Applied Scientist, or Research Engineer.
Proficiency in Python and at least one of PyTorch, TensorFlow, or JAX.
Experience building production-grade end-to-end ML pipelines (data ingestion, training, deployment).
Experience implementing and fine-tuning LLMs, embeddings, and generative models for real-world applications.
Hands-on experience with distributed training/inference and scalable ML systems.
Experience with cloud platforms (AWS, GCP, or Azure) and ML tooling (MLflow, Weights & Biases) for experimentation and model tracking.
Strong collaboration skills; ability to work cross-functionally with data and product teams.
Bias for action, autonomy, and eagerness to shape something from scratch.

Founding Machine Learning Engineer

Key skills