Senior Machine Learning Engineer

What you'll do

• Design and own end-to-end data pipelines for LLM training, post-training, evaluation, and continuous model improvement.
• Build and improve human-in-the-loop data labeling workflows including collection, task generation, and quality control.
• Develop scalable methods for synthetic data generation, augmentation, filtering, and validation to improve dataset diversity.
• Apply machine learning techniques to automate the process of data generation, labeling, filtering, and scoring.
• Build systems and metrics to measure dataset quality factors like label noise, bias, and distribution shifts.
• Create reliable infrastructure for processing large volumes of structured and unstructured data.
• Design experiments to analyze how dataset composition impacts downstream model performance.
• Translate research prototypes into robust, production-ready data and machine learning systems.

What we're looking for

• Bachelor's degree in a STEM field with 7+ years of experience, Master's with 4+ years, or PhD with 1+ year of research experience.
• 2+ years of hands-on experience building, curating, and scaling datasets for machine learning training and evaluation.
• 3+ years of professional programming experience using Python, C++, or Go in a production or research environment.
• 3+ years of experience using frameworks like PyTorch or TensorFlow to develop, train, evaluate, and deploy models.
• Expertise in curating and managing datasets for the LLM lifecycle, including synthetic data and post-training workflows (preferred).
• Proficiency in designing human-in-the-loop labeling systems and mitigating issues like noise, bias, and contamination (preferred).
• Demonstrated success using LLMs for data generation, model-assisted labeling, and evaluation (preferred).
• Strong technical foundation in distributed data processing frameworks such as Spark, Ray, or Beam (preferred).