Machine Learning Software Engineer II

Summary

Cambium Learning Group is an award-winning educational technology solutions leader dedicated to helping all students reach their potential through individualized instruction. The role involves designing and deploying custom machine learning solutions, bridging applied research and production systems while collaborating with a cross-functional team.

Responsibilities

• Full-Lifecycle ML Development: Lead the transition of machine learning models from theoretical prototypes into scalable, high-performance production systems
• AWS Cloud Architecture & Deployment: Architect and deploy ML solutions utilizing AWS ECS (Elastic Container Service) for containerized workloads and AWS Lambda for serverless, event-driven inference pipelines
• Model & Inference Optimization: Optimize PyTorch models for production deployment by converting them to ONNX formats. Apply advanced inference optimization techniques (quantization, pruning, ONNX Runtime) and memory-efficient attention mechanisms like Flash Attention to minimize latency and maximize throughput
• Infrastructure & Engineering Best Practices: Champion infrastructure best practices for machine learning systems, establishing reliable CI/CD pipelines, and ensuring robust, secure, and reproducible deployments across the AWS ecosystem
• Algorithm Engineering: Design, develop, and evaluate algorithms that generate descriptive, diagnostic, predictive, and prescriptive insights from both structured and unstructured data
• Robust Software Engineering: Write clean, efficient, and well-tested code. Complete rigorous testing, debugging, and documentation to ensure seamless installation and long-term maintenance
• Cross-Functional Collaboration: Actively participate in research discussions, requirements gathering, and system design alongside domain experts to build tailored scoring and ML solutions

Skills

• 2–5 years of industry experience in Machine Learning Engineering, Software Engineering, or Data Science, with a proven track record of architecting and deploying models to production
• Deep, hands-on experience with the AWS ecosystem, specifically AWS ECS and Lambda
• Solid understanding of containerization (Docker) and event-driven architectures
• Strong proficiency in modern programming languages used in ML (e.g., Python, C++, Java) and familiarity with industry-standard coding practices
• Hands-on experience with PyTorch and other machine learning libraries (e.g., Scikit-Learn, TensorFlow)
• Deep understanding of model optimization pipelines, including PyTorch to ONNX conversions, ONNX Runtime, and scaling attention mechanisms (e.g., Flash Attention)
• Experience working with large-scale computing frameworks, data analysis systems, and relational/non-relational databases
• Experience utilizing AWS SageMaker for managed model training and hosting
• Hands-on experience applying modern parameter-efficient fine-tuning methods (such as LoRA and qLoRA) to large language models
• Experience building, integrating, and deploying autonomous or semi-autonomous AI agents to automate complex workflows and connect ML models with external tools/APIs
• Proven experience and familiarity with deep learning technologies applied specifically to Natural Language Processing (NLP) and complex text-based modeling
• Experience collaborating with specialized researchers (e.g., psychometricians, statisticians) to operationalize complex mathematical concepts
• Experience implementing IaC using tools like Terraform or AWS CloudFormation
• Experience setting up comprehensive model monitoring systems to detect data drift, concept drift, and model degradation in production AWS environments

Qualifications

Must Haves

• 2–5 years of industry experience in Machine Learning Engineering, Software Engineering, or Data Science, with a proven track record of architecting and deploying models to production
• Deep, hands-on experience with the AWS ecosystem, specifically AWS ECS and Lambda
• Solid understanding of containerization (Docker) and event-driven architectures
• Strong proficiency in modern programming languages used in ML (e.g., Python, C++, Java) and familiarity with industry-standard coding practices
• Hands-on experience with PyTorch and other machine learning libraries (e.g., Scikit-Learn, TensorFlow)
• Deep understanding of model optimization pipelines, including PyTorch to ONNX conversions, ONNX Runtime, and scaling attention mechanisms (e.g., Flash Attention)
• Experience working with large-scale computing frameworks, data analysis systems, and relational/non-relational databases

Nice to Haves

• Experience utilizing AWS SageMaker for managed model training and hosting
• Hands-on experience applying modern parameter-efficient fine-tuning methods (such as LoRA and qLoRA) to large language models
• Experience building, integrating, and deploying autonomous or semi-autonomous AI agents to automate complex workflows and connect ML models with external tools/APIs
• Proven experience and familiarity with deep learning technologies applied specifically to Natural Language Processing (NLP) and complex text-based modeling
• Experience collaborating with specialized researchers (e.g., psychometricians, statisticians) to operationalize complex mathematical concepts
• Experience implementing IaC using tools like Terraform or AWS CloudFormation
• Experience setting up comprehensive model monitoring systems to detect data drift, concept drift, and model degradation in production AWS environments