Take on a hands-on role developing machine learning solutions that support real products and applications. As a Machine Learning Engineer III, you'll work across algorithm development, data pipeline design, model training, validation, deployment, and monitoring. You'll help move machine learning projects from early concepts into reliable, production-ready solutions. This opportunity is a strong fit for an experienced engineer who enjoys both the technical depth of machine learning and the collaboration required to bring projects across the finish line. You'll design proof-of-concept solutions, evaluate new approaches, work with teams beyond your immediate group, and help solve technical challenges that shape future products. Required Skills & Experience 5-8 years of related experience after completing a bachelor's degree, or a master's degree with 2-3 years of related experience Experience implementing, refining, and validating machine learning or deep learning algorithms Strong programming and software development skills Familiarity with Python, Java, Scala, or similar programming languages Experience designing data pipelines for ingestion, validation, cleaning, and monitoring Bachelor's degree in Computer Science, Computer Engineering, Mathematics, or a related technical discipline-or equivalent industry experience Desired Skills & Experience Experience with data mining or statistical analysis tools Experience deploying and monitoring machine learning models in production Knowledge of Kafka, Spark, Docker, or similar data and cloud technologies Experience designing proof-of-concept solutions or contributing to technical studies Experience with model testing, accuracy evaluation, case studies, or performance reporting Experience collaborating with teams outside of an immediate work group What You Will Be Doing Implement, refine, and validate machine learning algorithms for products and applications Design and develop data pipelines for data ingestion, validation, cleaning, and monitoring Train models, evaluate accuracy, and deploy validated models into production Design proof-of-concept solutions and contribute to future product development studies Test and evaluate solutions through case studies, technical reviews, and performance reporting Collaborate with cross-functional teams to address technical issues and support project delivery Tech Breakdown 30% Machine Learning Algorithm and Model Development 25% Data Pipeline Design and Cloud Technologies 20% Model Training, Validation, Deployment, and Monitoring 15% Proofs of Concept, Testing, and Technical Evaluation 10% Documentation, Collaboration, and Technical Support