Machine Learning Engineer (Junior)

Pangram Labs; New York (NY); On-site; USD 90,000 - 130,000; Full time

Job summary

Pangram Labs in Downtown Brooklyn, NY is seeking a junior Machine Learning Engineer to build software supporting the ML development cycle—from data generation to training models, deployment, and monitoring production ML systems in real customer environments.

This in‑person role sits in our Downtown Brooklyn office. The team is actively involved in research and publishing, with frequent opportunities to contribute ideas and innovations to the squad.

Qualifications

• BS or MS in CS or related field.
• Practical deep learning experience via internships or projects.
• Strong Python skills and ML framework knowledge.
• Solid understanding of transformers/LLMs.
• Ability to bridge research and engineering.

Responsibilities

• Build data pipelines and generate synthetic text for training.
• Manage distributed infrastructure for multi-GPU training.
• Profile and optimize training and inference code.
• Deploy inference pipelines for serving LLMs at scale.

Skills

Python programming

Deep learning

Transformers/LLMs

Research–engineering collaboration

Education

Bachelor's or Master's in CS or related

Tools

CUDA

DeepSpeed

vLLM

Spark/Beam

Airflow

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)