Lead Machine Learning Engineer, SharkNinja

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field
• 6+ years of experience building and deploying machine learning models in production environments
• Strong experience with Python and ML frameworks such as TensorFlow, PyTorch, or scikit-learn
• Solid understanding of supervised and unsupervised learning, model evaluation, and feature engineering
• Experience deploying ML models using cloud-based platforms and services (AWS, GCP, or Azure)
• Familiarity with MLOps practices, including CI/CD for ML, model monitoring, and retraining strategies
• Ability to clearly communicate complex technical concepts to both technical and non-technical partners
• Proven ability to work in fast-paced, cross-functional environments with a “progress over perfection” mindset

Desirable

• The Lead Machine Learning Engineer will play a critical hands-on role in designing, building, and deploying machine learning solutions that power SharkNinja’s next generation of consumer products, digital experiences, and operational capabilities
• This role combines deep technical execution with technical leadership, mentoring, and cross-functional collaboration. You will lead end-to-end ML initiatives—from problem framing and data exploration through production deployment—while helping to establish best practices for scalable, reliable ML at SharkNinja
• Lead the design, development, and deployment of production-grade machine learning models across consumer, digital, and operational use cases
• Partner with Product, Engineering, and Business stakeholders to translate real-world problems into ML-driven solutions with measurable impact
• Own end-to-end ML workflows, including data preparation, feature engineering, model training, evaluation, deployment, and monitoring
• Build and maintain scalable ML pipelines and services that integrate seamlessly with SharkNinja’s data and software platforms
• Provide technical leadership and mentorship to ML engineers and data scientists, raising the bar on model quality, reliability, and performance
• Drive best practices in model versioning, experimentation, monitoring, and lifecycle management
• Collaborate with Data Engineering and Platform teams to ensure ML solutions are secure, performant, and production-ready
• Contribute to technical design reviews and influence architectural decisions related to ML systems
• Stay current with emerging ML techniques and technologies, applying them thoughtfully to SharkNinja use cases