Lead Machine Learning Engineer

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.

Key Responsibilities

• 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.

Qualifications

Must-Haves

• 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.

Nice-to-Haves

• Experience with real-time or embedded ML systems.
• Exposure to recommendation systems, personalization, forecasting, or computer vision.
• Experience working in consumer products, IoT, or e-commerce environments.
• Familiarity with data orchestration tools (e.g., Airflow) and ML platforms (e.g., SageMaker, Databricks).