Senior Machine Learning Engineer, AISWP (Hybrid)
Core
Building scalable data pipelines, human-in-the-loop labeling systems, and synthetic data generation to create high-quality training and evaluation datasets for Large Language Models (LLMs) and AI systems.
Role type
Senior Machine Learning Engineer (Data & ML Infrastructure)
Builds
End-to-end data pipelines for ML/LLM training, post-training, evaluation, and continuous model improvement; human-in-the-loop labeling workflows; synthetic data generation systems.
Domain
Networking, Generative AI, Large-Scale Data Systems
Deliverable
production ML models
Required skills
Python, C++, or Go programming; Machine Learning frameworks (PyTorch, TensorFlow); Dataset curation and scaling; Human-in-the-loop labeling system design; Synthetic data generation and augmentation; Distributed data processing (Spark, Ray, Beam); LLM application for data tasks; Metrics development for dataset quality.
Preferred skills
LLM lifecycle expertise (SFT, RLHF); Mitigating dataset failure modes (noise, bias, contamination); Model-assisted labeling and evaluation; Research-engineering mindset.
Technologies
PyTorch, TensorFlow, Spark, Ray, Beam, Python, C++, Go
Responsibilities
Design and own end-to-end data pipelines for ML and LLM training and evaluation; Build human-in-the-loop data labeling pipelines with quality control; Develop scalable approaches for synthetic data generation and validation; Apply LLMs to automate data generation, labeling, and evaluation; Develop systems and metrics to measure dataset quality and distribution shifts; Translate research prototypes into production-ready data and ML systems; Provide technical leadership through design reviews and mentoring.
Seniority
Senior, hands-on IC with technical leadership