Machine Learning Engineer / AI Systems Engineer
New
A
Amira LearningEdtech
Fully remote; work from any U.S. locationFull-TimeMiddle
Salary not disclosed
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Job Details
- Experience
- 3+ years in ML engineering, AI systems development, or software engineering related occupation, in a startup environment.
- Required Skills
- PythonArtificial IntelligenceMachine LearningData engineeringNLPLLM
Requirements
- Master’s degree in Computer Science or related field.
- 3+ years of experience in ML engineering, AI systems development, or software engineering in a startup environment.
- Proven experience designing, developing, and deploying ML/AI systems for real-time production-grade educational or adaptive learning platforms.
- Proficiency in speech recognition, natural language processing (NLP), and large language model (LLM) integration.
- Experience architecting scalable Python backend services optimized for low-latency, high-throughput AI inference.
- Experience applying machine learning techniques including semantic analysis, multi-label classification, and predictive modeling.
- Experience engineering large-scale distributed data systems and cloud-native pipelines for model training.
- Experience conducting model validation, bias analysis, and performance optimization.
- Advanced programming skills in Python for development of production ML pipelines and API services.
- Experience integrating third-party AI services with proprietary models.
Responsibilities
- Design, deploy, and maintain automated educational evaluation systems leveraging speech recognition, natural language processing (NLP), and large language models (LLMs).
- Integrate, fine-tune, and optimize third-party AI services, open-weight LLMs, and proprietary machine learning models.
- Architect and build end-to-end machine learning pipelines encompassing data extraction, semantic analysis, multi-label classification, and predictive modeling.
- Design, develop, and maintain AI/ML systems purpose-built for real-time, low-latency educational platforms.
- Architect scalable Python backend services engineered for sub-second inference in production AI applications.
- Conduct model validation, bias analysis, and performance optimization for reliability across diverse populations.
- Implement cloud-native data engineering solutions to process, transform, and serve large-scale educational datasets.
- Collaborate with cross-functional engineering, data science, and product teams to translate requirements into AI architectures.
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