Machine Learning Engineer

Pangram LabsNew York City, NY
8hOnsite

About The Position

Pangram Labs’ mission is to protect authenticity by building the world’s best AI detection systems. We publish research on state-of-the-art AI detection techniques and algorithms, and build products to bring our technology into people's daily lives. We are looking for high agency, mission-driven, and passionate builders to join our team. All roles are in person in Brooklyn, New York. About the Role Pangram Labs is hiring strong Machine Learning Engineers at all levels to join our team. In this role, you will build software to support the machine learning development cycle from data generation, to training models, to deployment and monitoring production machine learning systems in real customer environments. At Pangram, ML engineers are highly involved in the research effort, are involved in publishing research, and regularly contribute ideas and innovations to the team. However, formal research experience is not necessary. This is an in-person role in our office in Downtown Brooklyn, NYC.

Requirements

  • B.S./M.S. in Computer Science or related areas
  • Strong programming skills in Python and modern ML frameworks
  • Excellent understanding of transformers and LLM fundamentals
  • Comfort working across research and engineering boundaries

Nice To Haves

  • Experience with NVIDIA GPU programming and CUDA
  • Experience with distributed training frameworks, such as DeepSpeed, FSDL, Ray
  • Experience with inference frameworks like vLLM
  • Experience with large-scale data processing (Spark, Beam) and orchestration (Airflow)
  • Experience with MLOps and experiment tracking
  • Experience with DevOps tools
  • Familiarity with cloud-based infrastructure (AWS/GCP)

Responsibilities

  • Build robust data pipelines that mine the Internet at scale and generate millions of synthetic text examples for training detection models
  • Manage distributed infrastructure for multi-GPU LLM training
  • Profiling and optimizing training and inference code
  • Deploy efficient inference pipelines for serving LLMs at scale
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