Machine Learning Engineer III

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