Senior AI Engineer, Paper

• Bachelor’s or Master’s degree in Computer Science, Engineering, Statistics, or related field
• 5+ years of experience developing production-grade AI/ML systems
• Proven success delivering both trained (supervised, unsupervised, etc.) and prompt-based (LLM-powered, few-shot, etc.) solutions
• Experience evaluating and monitoring model performance with robust testing and validation protocols
• Strong programming skills in Python and experience with frameworks such as PyTorch, TensorFlow, Hugging Face, LangChain, or OpenAI APIs
• Deep understanding of the full ML lifecycle—data pipelines, model training, inference, and monitoring
• Experience operationalizing LLMs and prompt-engineering workflows
• Familiarity with distributed systems and cloud infrastructure for AI workloads (e.g., AWS, GCP, Azure)
• Experience with multi-modal or retrieval-augmented generation (RAG) systems is a plus
• Collaboration & Impact:
• Excellent communication and collaboration skills; able to work effectively in cross-functional environments
• Strong problem-solving abilities with a focus on translating complex AI concepts into real-world product value
• Proactive, detail-oriented, and driven by continuous learning and experimentation

Desirable

• We are seeking a Senior AI Engineer to design, develop, and operationalize the first B2B In-Classroom Tutor AI. Using cutting-edge AI solutions that power critical product features across our platform
• You’ll work hands-on across the full machine learning lifecycle. From experimentation to deployment, you will be helping to shape the future of AI at scale for millions of learners and educators
• In this role, you’ll collaborate closely with Product, Data, and Engineering teams to build both trained (supervised and unsupervised) and prompt-based AI systems . You’ll balance technical depth with creativity, ensuring that our AI implementations are effective, scalable, and responsible
• AI Solution Development: Design, implement, and deploy AI-powered features, including model training, fine-tuning, and prompt engineering workflows (e.g., leveraging LLMs and multi-modal architectures)
• Collaboration & Delivery: Work cross-functionally with Product Managers, Software Engineers, and Data Scientists to translate product requirements into robust, production-ready AI solutions
• Technical Execution: Own the end-to-end lifecycle of AI initiatives—from data manipulation and model experimentation to deployment, monitoring, and continuous improvement
• Performance & Reliability: Optimize models and infrastructure for scalability, latency, and cost efficiency. Partner with DevOps and MLOps to ensure reliable and maintainable AI pipelines
• Innovation & Research: Stay current with the latest AI advancements (e.g., LLMs, RAG, multi-modal models) and drive rapid experimentation to identify opportunities for impactful innovation