Cambium Learning Group

 

 https://www.cambiumlearning.com/careers/

Example posting: 

 

Job Overview:

We are seeking a talented Machine Learning Engineer II to join our CAI machine learning and scoring development team. In this role, you will be the crucial bridge between applied research and production systems. Working alongside a cross‑functional group of mathematicians, computer scientists, psychometricians, and statisticians, you will design and deploy custom machine learning solutions for our clients and internal platforms.

The ideal candidate is a full‑stack ML practitioner who is equally comfortable discussing algorithmic design with researchers and architecting scalable, low‑latency production systems. You will own the full software development lifecycle—transforming research prototypes into optimized, production‑ready solutions using modern AWS infrastructure such as SageMaker, ECS, and Lambda, with an emphasis on high‑throughput inference and PyTorch‑to‑ONNX model optimization.

Job 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.