Machine Learning Engineer

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.

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./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 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)

We are a small, tight-knit team of researchers and engineers who care deeply about our work and each other. We believe that a small group of highly talented, empowered, and efficient people can outperform teams many times their size. Everyone on the team has meaningful ownership over the technical and strategic direction of the company.

Pangram is primarily an in-person company. We are high-touch, high-energy, and extremely collaborative by nature and choose to spend time together to do our best thinking and innovation.