This role involves using generative models and optimization tools and methods to produce diverse and representative data when real-world data is scarce sensitive or prohibitively expensive to collect. This researcher collaborates closely with other ML scientists and data scientists to understand data requirements optimize generation pipelines and ensure the synthetic data supports model generalization and addition to research and technical implementation the role also requires developing quality control mechanisms such as human-in-the-loop feedback loops to ensure that synthetic datasets are valid. This work will support applications in diverse domains.
Strong foundation in ML fundamentals.
Experience or proven interest in designing and implementing AI-driven approaches to data generation.
Experience developing evaluation metrics and systems.
Proficiency in software engineering best practices.
Proficiency in statistical analysis and data visualization.
Strong proficiency in Python.
Strong proficiency in PyTorch TensorFlow or Jax.
Excellent communication skills with a proven ability to engage diverse stakeholders.
Experience with MLOps standards including containerization orchestration (e.g. Kubernetes) and CI/CD.
5 years with a Masters degree 3 years with a PhD or equivalent practical experience.
Experience building scalable data generation pipelines and human-in-the-loop workflows
Experience developing and owning high-impact developer-facing systems and tools.
Experience with LLM orchestration frameworks such as Lanchain and DSPy
Experience adapting and aligning LLMs through various training strategies e.g. continued pre-training supervised fine-tuning and reinforcement learning.
Expertise in uncertainty estimation and calibration automated prompt optimization active learning or related problem spaces.
Experience with large-scale data processing frameworks e.g. Spark PySpark Dask or Ray.
Track record of contributions to open-source ML projects or publications in top-tier ML conferences (e.g. NeurIPS ICML ACL).
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