You will design and operate the end-to-end data lifecycle that supports training, fine-tuning, and evaluation of large language models and diffusion models. Day to day, that means building distributed pipelines that clean, deduplicate, and enrich massive multi-modal datasets; standing up synthetic data generation pipelines with LLM-based generators, validators, and judges; and engineering agentic workflows — multi-step, tool-using LLM systems — that scale human judgment across far more data than a human team could review directly.
Minimum Qualifications
Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, or a related engineering field
5+ years of hands-on experience applying NLP, computer vision, or multimodal machine learning to production systems, including experience with large-scale data pipelines
Preferred Qualifications
Experience prompting, fine-tuning, and evaluating LLMs or vision-language/diffusion models for specific downstream tasks
Experience with parameter-efficient fine-tuning (LoRA/QLoRA), preference optimization (RLHF, DPO), RAG and retrieval-based data curation
Experience designing, evaluating and productionizing data science algorithms used for data curation, data filtering, deduplication and data down-selection.
Experience designing controlled experiments or ablation studies to understand effect of data on model quality
Experience building agentic or multi-agent, tool-using LLM systems and human-in-the-loop workflows, calibrating judges against human labels, and mitigating failure modes
Strong software engineering skills, with 5+ years in Python, workflow orchestration (Airflow), cloud platforms (AWS, GCP), distributed data processing frameworks (Spark, Ray, or similar) at large scale
Publication record or open-source contributions in NLP, computer vision, or generative AI is a plus
Familiarity with privacy-preserving data techniques (e.g., differential privacy, on-device data constraints) is a plus