Machine Learning Engineer / AI Systems Engineer

Summary

Amira Learning is a leader in third-generation edtech focused on accelerating literacy outcomes through AI. The Machine Learning Engineer / AI Systems Engineer will design and maintain educational evaluation systems, optimize AI services, and develop scalable machine learning pipelines to enhance real-time learning experiences.

Responsibilities

• Design, deploy, and maintain automated educational evaluation systems leveraging speech recognition, natural language processing (NLP), and large language models (LLMs) to analyze and assess real-time user performance and feedback
• Integrate, fine-tune, and optimize third-party AI services, open-weight LLMs, and proprietary machine learning models to power conversational and adaptive learning experiences tailored to individual user proficiency
• Architect and build end-to-end machine learning pipelines encompassing data extraction, semantic analysis, multi-label classification, and predictive modeling for real-time educational content delivery
• Design, develop, and maintain AI/ML systems purpose-built for real-time, low-latency educational platforms serving concurrent users at scale
• Architect scalable Python backend services deployed on cloud infrastructure, engineered specifically for sub-second inference in production AI applications
• Conduct rigorous model validation, bias analysis, and performance optimization to ensure accuracy, scalability, and reliability of machine learning systems across diverse user populations
• Implement cloud-native data engineering solutions utilizing distributed systems and databases 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, production-grade AI architectures
• Author technical design documents, algorithm specifications, and architecture decision records; communicate progress, risks, and trade-offs to technical and non-technical stakeholders

Skills

• Master's degree in Computer Science or related field
• 3+ years in ML engineering, AI systems development, or software engineering related occupation, in a startup environment
• Designing, developing, and deploying machine learning and artificial intelligence systems for real-time, production-grade educational or adaptive learning platforms, including speech recognition, natural language processing, and large language model integration
• Architecting scalable Python backend services optimized for low-latency, high-throughput AI inference serving concurrent users on cloud infrastructure
• Applying machine learning techniques — including semantic analysis, multi-label classification, and predictive modeling — to build end-to-end automated evaluation and feedback systems
• Engineering large-scale distributed data systems and cloud-native pipelines for processing, transforming, and serving high-volume datasets for model training and real-time inference
• Conducting model validation, bias analysis, and performance optimization to ensure accuracy, scalability, and reliability across diverse user populations
• Programming in Python, including development of production ML pipelines, API services, and integration of third-party AI services with proprietary models

Qualifications

Must Haves

• Master's degree in Computer Science or related field
• 3+ years in ML engineering, AI systems development, or software engineering related occupation, in a startup environment
• Designing, developing, and deploying machine learning and artificial intelligence systems for real-time, production-grade educational or adaptive learning platforms, including speech recognition, natural language processing, and large language model integration
• Architecting scalable Python backend services optimized for low-latency, high-throughput AI inference serving concurrent users on cloud infrastructure
• Applying machine learning techniques — including semantic analysis, multi-label classification, and predictive modeling — to build end-to-end automated evaluation and feedback systems
• Engineering large-scale distributed data systems and cloud-native pipelines for processing, transforming, and serving high-volume datasets for model training and real-time inference
• Conducting model validation, bias analysis, and performance optimization to ensure accuracy, scalability, and reliability across diverse user populations
• Programming in Python, including development of production ML pipelines, API services, and integration of third-party AI services with proprietary models

Benefits

• Medical, dental, and vision benefits
• 401(k) with company matching
• Flexible time off
• Stock option ownership
• Cutting-edge work
• The opportunity to help children around the world reach their full potential
• Fully remote; work from any U.S. location (no relocation required)