Principal AI & Machine Learning Engineer

Summary

Join fabric, a next-generation commerce platform revolutionizing the industry, as a Principal AI/Machine Learning Engineer. Lead the development of data products, from raw data to production-ready solutions. This high-impact role involves owning AI/ML initiatives end-to-end, developing production-grade ML systems, and applying various techniques like deep learning and reinforcement learning. You will build scalable data products and lead MLOps efforts, collaborating with engineering and product teams. The ideal candidate possesses 8+ years of experience in AI/ML, a strong track record of building and scaling ML solutions, and full-stack ML expertise. A competitive compensation package, including benefits and equity options, is offered.

Requirements

• 8+ years in AI/ML role, with high-growth startup — you’ve built and launched multiple data products from scratch
• Track record of building and scaling ML solutions in real-world applications , such as recommendations, forecasting, fraud detection, clustering, and categorization
• Data Engineering -feature engineering/ store, distributed data processing
• Modeling -deep learning, classical ML, RL, unsupervised learning
• MLOps - deployment, monitoring, CI/CD, infrastructure
• Strong programming skills using Spark, Python, TensorFlow/PyTorch, MLFlow, Airflow, Docker, Kubernetes, etc
• Passion for building usable, scalable systems—not just research or models
• Advanced degree (PhD or MS) in CS, ECE, Statistics, Econometrics, Physics, or relevant industry experience
• Strong communication and leadership skills with the ability to mentor others and influence technical direction

Responsibilities

• Own AI/ML initiatives end-to-end: Frame problems, design solutions, build/train models, test and deploy to production
• Develop production-grade ML systems: Architect and implement pipelines for training, testing, serving, monitoring, and retraining
• Apply a wide range of techniques: Deep learning, reinforcement learning, supervised/unsupervised learning, linear/non-linear optimization, and probabilistic modeling
• Build scalable, intelligent data products: like recommendation engines, forecasting, fraud detection, search, NLP, categorization, personalization, and operations optimization
• Lead MLOps efforts: Establish and maintain infrastructure and tooling for quick experimentation, validation, and deployment
• Collaborate cross-functionally: Work with engineering & product teams to embed intelligence into user-facing products
• Drive innovation: Stay on top of new research and industry trends to inform AI/ML roadmap and technology choices

Benefits

• Competitive compensation packages
• PTO and Holiday plans
• Benefits packages which include Medical, Dental, Life, and Vision
• Wellness & Technology Programs
• Retirement Savings Plan