Paper is building the first B2B, in-classroom Voice + Video AI Tutor designed for real-world learning environments. They are seeking a Senior AI Engineer to architect and operationalize multimodal AI systems at production scale, owning end-to-end AI initiatives that power features used by millions of learners.

Responsibilities:

Design and deploy real-time voice and vision AI systems for classroom environments
Evaluate tradeoffs between fine-tuning, RAG pipelines, prompt-based approaches, and tool-using agents
Build scalable inference systems with strict latency and cost constraints
Fine-tune and evaluate LLMs, ASR models, and vision/multimodal architectures
Design robust evaluation frameworks (offline + online)
Implement safety guardrails and bias mitigation strategies
Own the full ML lifecycle: data ingestion, training, validation, deployment, monitoring, and iteration
Optimize infrastructure for GPU utilization, latency, and cost efficiency
Partner with MLOps to build reliable, versioned AI pipelines
Work closely with Product and Engineering to translate classroom needs into production-ready AI systems
Clearly communicate tradeoffs between model complexity, performance, and cost
Contribute to long-term AI roadmap and architecture decisions

Requirements:

5+ years building and deploying production AI/ML systems
Hands-on experience operationalizing LLM-based systems (RAG, agents, prompt workflows, fine-tuning)
Experience with multimodal or vision systems
Demonstrated ownership of AI systems in production (monitoring, evaluation, iteration)
Strong Python proficiency and experience with PyTorch or TensorFlow
Experience working with distributed/cloud infrastructure for ML workloads
Experience building real-time or low-latency ML systems
Experience working with educational data or regulated environments (FERPA/COPPA)
Experience designing evaluation frameworks for generative systems
Background in optimizing ML systems for cost at scale

Paper

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Engineering

Senior AI Engineer