AI/ML Engineer (Entry Position)

Pragmatike•Cambridge, MA

14h•Onsite

About The Position

Pragmatike is hiring on behalf of a fast-growing AI startup recognized as a Top 10 GenAI company by GTM Capital , founded by MIT CSAIL researchers . We are looking for an AI/ML Engineer (Entry Position) to join a growing AI engineering team building and deploying real-world AI systems used by Fortune 500 customers . You will work closely with senior AI, ML, and platform engineers on the development, optimization, and deployment of machine learning models in production environments. This role is intended for engineers with early professional experience who already have hands-on exposure to applied machine learning and want to grow quickly by working on real, production-scale AI systems.

Requirements

Bachelors or Masters degree in Computer Science, Engineering, Mathematics, or a related field
2+ years of professional experience working on AI, ML, or data-driven systems (industry, research, or applied internships)
Excellent academic background , including strong grades and demonstrated university-level achievements
Evidence of academic or professional excellence , such as scholarships, honors, awards, competitive programs, or impactful projects
Solid understanding of machine learning fundamentals (supervised/unsupervised learning, evaluation metrics)
Hands-on experience with ML libraries such as PyTorch, TensorFlow, or scikit-learn (required)
Strong proficiency in Python
Strong problem-solving skills and the ability to work independently

Nice To Haves

Experience with deep learning, LLMs, or modern AI architectures
Experience working with real-world, noisy datasets in production
Familiarity with cloud platforms (AWS, GCP, or Azure)
Exposure to MLOps tools, model deployment, or monitoring
Publications, research experience, or participation in AI/ML competitions

Responsibilities

Build, train, and evaluate machine learning models in collaboration with senior engineers
Contribute to deploying ML models into production systems
Work with structured and unstructured datasets to support training and inference pipelines
Improve model performance, reliability, and scalability through experimentation
Implement and maintain ML code primarily in Python
Monitor model behavior and assist in debugging production issues
Participate in model iteration, validation, and performance analysis
Collaborate with backend and platform teams to integrate AI components into products