• Shape and build high-quality, scalable software solutions that support Amplify’s learning platform. • Develop back-end systems, including automated tests and related tooling. • Review code from other engineers, applying a pragmatic and detail-oriented approach to solving complex problems. • Collaborate with DevOps to develop, manage, and monitor deployment processes and infrastructure. • Ensure software meets the needs of students and performs reliably in real classroom environments, including proactive testing and system monitoring. • Participate in a collaborative engineering culture through code reviews and learning sessions that promote best practices.

• Build high-quality, robust, scalable software solutions that help Amplify build and maintain a best-in-class learning platform. • Develop code across front-end and back-end components, including automated tests and related tooling. • Reviewing code from other engineers on the team, bringing your keen eye for pragmatic and elegant solutions to challenging problems. • Collaborate with our DevOps team to develop, manage, and monitor our deployment processes and infrastructure. • Ensuring that our software meets the needs of all students and works reliably in real classrooms. Engaging in proactive testing and monitoring of our systems and guaranteeing a good experience for our end users. • Participating in a collaborative learning environment within Amplify Engineering: reviewing code from other engineers and participating in learning sessions to foster best practices and engineering culture. • Providing tools for teachers and students to effectively assess a child's reading ability. • Creating reports on student assessments with tools and recommendations for improving outcomes • Improving APIs to allow teacher and administrators' tools to perform to the highest standards of speed and reliability

• Building and maintaining the Shadeform GPU platform • Building out automated, reusable services for customers • Work on novel solutions to managing constrained and decentralized GPUs

• Architect and implement a robust agentic framework that supports tool use, context retrieval, memory, and planning • Build intelligent, modular agents that automate investigative tasks and augment analyst decision-making • Extend and scale our LLM infrastructure (e.g. OpenAI, Anthropic, local models), including prompt engineering, RAG, and evaluation loops • Design safe, observable, and auditable agent behaviors — ensuring reliability in high-sensitivity environments • Evaluate performance across metrics like reasoning, latency, success rate, and hallucination, and iterate based on user feedback and system telemetry • Contribute to a culture of high ownership, rapid experimentation, and ethical AI deployment