Job description

A Note on applying to Pangram: At Pangram, we firmly believe that genuine human expression, and our ability to communicate with each other online and through the written word, is a fundamental right- and a value worth preserving. Please don't use AI to answer any of the questions as part of your application - we want to hear from you! About Pangram Labs Pangram Labs’ mission is to protect authenticity and preserve human connection in the context of the generative AI era. We build 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 provide tools for individuals, institutions and companies to detect the use of AI in written content. Pangram is a recognized thought leader in the space of AI detection. Our CEO & Co-founder Max Spero has been named as one of Time Magazine’s 100 Most Influential People in AI time.com and Pangram has been named one of Time’s Best Inventions of 2026 time.com. We are looking for high agency, mission-driven, and passionate builders to join our team! About the Role As a junior Machine Learning Engineer at Pangram Labs, 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 a full-time, on-site role in our Brooklyn office reporting to our CTO. What You'll Do - 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 Qualifications You are a great candidate for this role if you have: - 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 You are an exceptional candidate if you also 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)

Requirements

Must-haves

At Pangram, we firmly believe that genuine human expression, and our ability to communicate with each other online and through the written word, is a fundamental right- and a value worth preserving.
Please don't use AI to answer any of the questions as part of your application - we want to hear from you!
This is a full-time, on-site role in our Brooklyn office reporting to our CTO.
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
You are a great candidate for this role if you have:
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-haves

You are an exceptional candidate if you also 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)

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningmlllmpythonawsgcp

Platform: ashby
Effort: quick