Machine Learning Engineer / AI Systems Engineer

About the role

About Us:

Amira Learning accelerates literacy outcomes by delivering the latest reading and neuroscience with AI. As the leader in third-generation edtech, Amira listens to students read out loud, assesses mastery, helps teachers supplement instruction and delivers 1:1 tutoring. Validated by independent university and SEA efficacy research, Amira is the only AI literacy platform proven to achieve gains surpassing 1:1 human tutoring, consistently delivering effect sizes over 0.4.

Rooted in over thirty years of research, Amira is the first, foremost, and only proven Intelligent Assistant for teachers and AI Reading Tutor for students. The platform serves as a school district’s Intelligent Growth Engine, driving instructional coherence by unifying assessment, instruction, and tutoring around the chosen curriculum.

Unlike any other edtech tool, Amira continuously identifies each student’s skill gaps and collaborates with teachers to build lesson plans aligned with district curricula, pulling directly from the district’s high-quality instructional materials. Teachers can finally differentiate instruction with evidence and ease, and students get the 1:1 practice they specifically need, whether they are excelling or working below grade level.

Trusted by more than 2,000 districts and working in partnership with twelve state education agencies, Amira is helping 3.5 million students worldwide become motivated and masterful readers.

Essential Functions:

- Design, develop, and maintain automated educational evaluation and learning systems leveraging speech recognition, NLP, and large language models (LLMs) to assess real‑time user performance and feedback.

- Integrate and fine‑tune third‑party AI services, open‑weight LLMs, and proprietary models to deliver adaptive, conversational learning experiences personalized to user proficiency.

- Architect and implement end‑to‑end ML pipelines for data extraction, semantic analysis, multi‑label classification, and predictive modeling supporting real‑time content delivery.

- Build scalable Python backend services and cloud‑native infrastructure engineered for low‑latency inference in production AI environments.

- Conduct rigorous model validation and bias analysis to ensure accuracy, scalability, and reliability across diverse user populations.

- Develop distributed data engineering solutions to process, transform, and serve large‑scale educational datasets for model training and real‑time inference.

- Collaborate with cross‑functional engineering, data science, and product teams to translate complex business requirements into robust AI architectures.

- Author technical design documents, algorithm specifications, and architecture decision records; communicate progress, risks, and trade‑offs to technical and non‑technical stakeholders. Fully remote; work from any U.S. location (no relocation required)

Qualifications (Education and Experience):