Machine Learning Software Engineer II

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. Job Requirements: Experience: 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. Cloud & MLOps Infrastructure: Deep, hands-on experience with the AWS ecosystem, specifically AWS ECS and Lambda . Solid understanding of containerization (Docker) and event-driven architectures. Programming Proficiency: Strong proficiency in modern programming languages used in ML (e.g., Python, C++, Java) and familiarity with industry-standard coding practices. ML Frameworks & Advanced Optimization: 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 ). Data Systems: Experience working with large-scale computing frameworks, data analysis systems, and relational/non-relational databases. Nice to Have's: AWS SageMaker: Experience utilizing AWS SageMaker for managed model training and hosting. Advanced LLMOps & Fine-Tuning: Hands-on experience applying modern parameter-efficient fine-tuning methods (such as LoRA and qLoRA ) to large language models. AI Agents: Experience building, integrating, and deploying autonomous or semi-autonomous AI agents to automate complex workflows and connect ML models with external tools/APIs. NLP Expertise: Proven experience and familiarity with deep learning technologies applied specifically to Natural Language Processing (NLP) and complex text-based modeling. Cross-Disciplinary Collaboration: Experience collaborating with specialized researchers (e.g., psychometricians, statisticians) to operationalize complex mathematical concepts. Infrastructure as Code: Experience implementing IaC using tools like Terraform or AWS CloudFormation. Model Monitoring: Experience setting up comprehensive model monitoring systems to detect data drift, concept drift, and model degradation in production AWS environments. . To learn more about our organization and the exciting work we do, visit Remote First Work Environment
Our Remote First approach gives employees the flexibility and trust they need to effectively balance work with life. It creates a culture in which all employees are valued and where success is measured in results. It allows us to work collaboratively, inclusively and for greater positive impact, regardless of our individual locations. If you will be working remotely, either occasionally or on a permanent basis, you must have a reliable internet connection through a cable or fiber-optic broadband service with minimum speeds of 10 Mbps download and 5 Mbps upload. The successful candidate will be expected to actively participate in video-based interviews during the recruiting process and ongoing virtual meetings with their camera on, as part of their role. To maintain confidentiality and ensure a fair evaluation process, the use of note-taking tools, reference materials, or AI-powered tools (including generative AI, language models, or similar technologies) during interviews or other selection activities is prohibited unless prior written approval has been obtained from the People Experience team. If you require an exception for medical, accessibility, or other reasons, please contact your Talent Acquisition team member to discuss accommodations in advance. As part of our Remote-First benefits, Cambium offers reimbursement to help cover the cost of setting up your home or remote office. An Equal Opportunity Employer We are dedicated to fostering a culture that celebrates unique backgrounds, ideas, and experiences. All qualified applicants will receive consideration for employment without discrimination on the basis of race, color, age, religion, sex (including pregnancy, gender, gender identity/expression, or sexual orientation), national origin, protected veteran status, disability, or genetic information (including family medical history). We will provide reasonable accommodations for qualified individuals with disabilities. You may request an accommodation during the recruiting process with your Talent Acquisition team member to discuss accommodations in advance.