Principal Machine Learning Engineer

The Principal Machine Learning Engineer is a senior technical authority responsible for shaping SharkNinja's machine learning strategy, architecture, and long-term technical direction. This role operates as a force multiplier across teams-solving the hardest problems, defining standards, and influencing how ML is built, deployed, and scaled across the enterprise. You will lead high-impact initiatives, guide architectural decisions, and mentor senior engineers while remaining deeply hands-on with critical systems.

Key Responsibilities

• Define and drive the technical vision and architecture for machine learning systems across SharkNinja.
• Lead the design of scalable, reliable, and secure ML platforms and services that support global products and operations.
• Own the most complex ML initiatives, from early exploration through enterprise-scale deployment.
• Partner with executive stakeholders, product leaders, and engineering leadership to identify where ML can deliver outsized business impact.
• Establish best practices, standards, and reference architectures for ML development, deployment, and governance.
• Serve as a technical mentor and advisor to ML engineers, data scientists, and software engineers across teams.
• Influence data strategy, tooling decisions, and platform investments related to ML and AI.
• Evaluate and integrate emerging ML technologies, frameworks, and approaches where they provide clear value.
• Represent SharkNinja as a thought leader in applied machine learning, internally and externally as needed.

Qualifications

Must-Haves

• Bachelor's, Master's, or PhD in Computer Science, Engineering, Data Science, or a related technical field.
• 10+ years of experience in machine learning, with significant experience designing and deploying large-scale ML systems.
• Deep expertise in multiple ML domains (e.g., predictive modeling, personalization, optimization, computer vision, or NLP).
• Strong proficiency in Python and ML frameworks such as PyTorch or TensorFlow.
• Extensive experience with cloud-native ML architectures and MLOps at scale.
• Proven track record of influencing technical strategy and architectural decisions across organizations.
• Exceptional problem-solving skills and the ability to operate effectively in ambiguous, high-impact situations.
• Strong executive-level communication skills, with the ability to translate complex technical concepts into business value.

Nice-to-Haves

• Experience leading ML strategy in a global, multi-brand, or consumer-facing organization.
• Background in IoT, connected devices, or embedded ML.
• Experience building internal ML platforms or enabling ML at scale across multiple teams.
• Publications, patents, or open-source contributions in ML or AI.

Core Competencies & Behaviors

• Enterprise-level technical leadership
• Strategic thinking with hands-on execution
• Influence without authority
• Consumer-obsessed innovation
• Relentless pursuit of excellence

Tools / Technologies

Python, PyTorch, TensorFlow, advanced ML platforms, cloud-native architectures, MLOps frameworks, distributed data systems