Why This Role Stands Out
This role offers incredible opportunities to contribute to cutting-edge machine learning development and research within a reputable company. You'll thrive here if you're a proactive individual with a strong foundation in Python and deep learning, eager to build impactful software and grow your skills. Apply today to join an innovative team and shape the future of AI!
Quick Overview
Job Description
Pangram Labs is hiring for a strong junior Machine Learning Engineer. 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.
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
Requirements:
B.S. or M.S. in Computer Science or related areas
Practical experience with deep learning: internships, undergrad or masters’ level research projects in an academic lab, Kaggle competitions, or interesting side projects
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 have
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)
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