AI Manager, Paper

• We are seeking an experienced Machine Learning Manager to lead a team of ML engineers and data scientists
• Bachelor’s or Master’s degree in Computer Science, Engineering, Statistics, or related field
• 7+ years of experience building production-grade AI/ML solutions, with 3+ years in a formal team leadership or management role
• Demonstrated success delivering both trained (supervised, unsupervised, etc.), prompt-based (LLM-powered, few-shot, etc.), and multi-modal AI systems
• Demonstrated expertise in model evaluation—designing and implementing both manual review protocols and automated test suites
• Hands-on expertise in Python and common AI/ML frameworks (PyTorch, TensorFlow, Hugging Face, OpenAI, LangChain, etc.)
• Deep understanding of end-to-end ML lifecycle: data ingestion, model training, evaluation, deployment, and monitoring
• Experience operationalizing LLMs and integrating prompt engineering workflows
• Proven experience designing, training, and deploying multi-modal models leveraging diverse data sources (text, image, audio, video)
• Excellent mentorship, communication, and stakeholder management skills
• Proven ability to lead with empathy, clarity, and accountability
• Track record of managing cross-functional projects from ideation to production
• Ability to align technical vision with business impact in a fast-paced environment

Desirable

• This team is responsible for developing and operationalizing AI solutions (trained and prompt-based) that power critical product features across our platform. As Senior Manager, you will drive project execution, own delivery for AI/ML initiatives, and mentor a group of engineers and ML specialists
• You’ll closely partner with Product, Data, and Engineering teams to ship effective, scalable, and ethical AI solutions for millions of learners and educators
• Technical Leadership: Oversee the design, implementation, and deployment of AI-powered features, including model training and prompt-based frameworks (e.g., leveraging LLMs). Guide best practices for AI development, MLOps, and system integration
• Team Management: Grow, mentor, and manage a talented team of ML engineers and data scientists. Foster a collaborative, inclusive, and high-performance work environment
• Project & Stakeholder Management: Ensure timely and rigorous delivery of AI initiatives. Work with Product and Engineering leads to define requirements, plan roadmaps, estimate effort, and prioritize work
• Innovation & Quality: Encourage experimentation, rapid prototyping, and continuous improvement. Ensure robust evaluation and monitoring of AI solutions
• Scale & Reliability: Partner with DevOps and Developers to build scalable, reliable, and cost-efficient AI services
• Ethics & Compliance: Champion best practices for responsible AI (privacy, fairness, safety). Stay abreast of regulatory requirements and lead compliance efforts for AI solutions