Machine Learning Engineer / Research Scientist - Modeling (Senior/Staff)

About rednote

We’re a lifestyle community company with a simple but powerful mission: Inspire life. We believe technology can help people discover new ideas, share meaningful moments, build communities, and express themselves creatively. Our platform is designed for everyday wonder—whether that means finding inspiration, learning something new, or connecting with people and content that matter.

About the role

We are looking for an innovative and driven Machine Learning Engineer or Research Scientist (Senior or Staff level) to join our Algorithm team in NY building out new products. In this role, you will report into our Global ML Lead and engage in 0->1 effort in building, scaling, and optimizing our feed ranking and personalization systems. We are looking for someone excited to do more than tweaking existing models, instead they will work to design a feed with product & UXR that users love. Creating, owning, and improving ML models (classic and LLM) to solve interesting problems such as engagement, content discovery, improving early user retention, should excite you.

Key Responsibilities

• Architect, train, and deploy our 0-1 core machine learning models (e.g., Transformers, Multi-modal Networks, Graph Neural Networks, Large Language Models) to generate rich embeddings for users, creators, and diverse content.
• Build content understanding and safety models across text, images, video, and audio: classification and tagging, quality and originality signals, and the multimodal representations that retrieval runs on.
• Partner closely with the Feed Ranking and Backend engineering teams to ensure your models and representations efficiently power downstream retrieval and personalization systems.
• Fine tune & post train LLMs, owning training pipelines and eval flows.
• Design rigorous offline evaluation frameworks to measure representation quality, and collaborate on A/B tests to tie underlying model improvements directly to engagement and retention metrics.
• Stay up-to-date with the latest research in deep learning, foundation models, and multi-modal AI, bringing innovative architectural ideas and industry best practices to the team.

Qualifications

• Bachelor’s or higher degree in computer science, machine learning, statistics, math, or a similarly quantitative field.
• 5+ years of industry experience as a machine learning engineer or research scientist focused on deep learning and model architecture (2+ years if coming from a relevant PhD), having trained and shipped multiple models to production.
• Proven hands-on experience building, training, and deploying Deep Learning, NLP, Computer Vision, or Representation Learning models from scratch. Experiences working at a social discovery or content-heavy platform is a plus.
• Deep proficiency in Python & PyTorch, fine-tuning techniques, and multimodal models
• Experience with large-scale data processing technologies
• A strong understanding of embedding spaces, representation learning, and how they apply to consumer social dynamics and UGC is preferred.
• Thrive in ambiguous, fast-paced environments, enjoy building end-to-end systems, and are biased towards action and product-attuned building.