Director, Machine Learning & AI

About Us 

SharkNinja is a global product design and technology company, with a diversified portfolio of 5-star rated lifestyle solutions that positively impact people’s lives in homes around the world. Powered by two trusted, global brands, Shark and Ninja, the company has a proven track record of bringing disruptive innovation to market, and developing one consumer product after another has allowed SharkNinja to enter multiple product categories, driving significant growth and market share gains. Headquartered in Needham, Massachusetts with more than 3,600+ associates, the company’s products are sold at key retailers, online and offline, and through distributors around the world.  

 

 

Position Summary

The Director of ML and AI leads SharkNinja’s enterprise-wide machine learning strategy, delivery, and operations. This role partners with the AI Product Manager to translate business priorities into scalable AI systems that deliver measurable business impact. The Director builds and manages high-performing ML engineering and MLOps teams, sets standards for responsible AI, and ensures SharkNinja’s AI platforms are secure, reliable, and cost-effective. The role is also accountable for leveraging Capex investments to deliver high-return AI capabilities and for managing key partnerships and vendor relationships to accelerate delivery.

Key Responsibilities

Strategy &Vision - Define and own the ML/AI strategy aligned with enterprise objectives, balancing near-term value and long-term scalability. Partner with AI Product Management on roadmap prioritization and resource allocation. Serve as executive advisor on AI/ML trends, vendor landscape, and technology adoption.

ML Engineering & Delivery-  Lead development of production-grade ML systems (predictive models, recommendation engines, generative AI apps). Build reusable components (feature stores, vector databases, evaluation frameworks). Ensure models meet KPIs for accuracy, latency, cost, and business impact. Directly manage the allocation of Capex funds to maximize ROI in platforms, tooling, and engineering capacity.

ML Ops &Platform Standards - Establish enterprise MLOps practices (CI/CD, automated testing, monitoring, retraining). Define standards for data quality, model observability, prompt/version management, and reproducibility. Partner with Security, Legal, and Data Governance to enforce safety, privacy, and compliance.

Build vs. Buy Lead technical due diligence and financial modeling for AI/ML investments. Collaborate with Product Management on vendor evaluations and build vs. buy decisions. Publish decision logs with risks and contingency plans.

Cross-Functional Integration-  Partner with Platform, Data Engineering, and Enterprise MDM to ensure AI solutions are built on trusted, standardized data. Lead technical design for API-first and event-driven integrations across ERP, CRM, and eCommerce systems.

Partners &Vendors-  Own relationships with key AI/ML technology vendors and service providers. Negotiate contracts, SLAs, and deliverables. Run proofs of value and ensure third-party contributions meet enterprise standards. Build strategic alliances with cloud providers, model vendors, and consultancies to accelerate delivery.

Talent & Culture - Recruit, mentor, and retain ML engineers, data scientists, and MLOps specialists. Foster a culture of experimentation, transparency, and measurable impact.

 

Required Qualifications

· 7+ years in applied machine learning and AI leadership roles.

· Proven record delivering enterprise-scale ML solutions tied to measurable outcomes.

· Deep expertise in ML engineering, data science, and MLOps.

· Hands-on experience with cloud AI/ML stacks (AWS, Azure, GCP), modern data platforms (Snowflake, dbt), and vector search/RAG.

· Demonstrated ability to lead build vs. buy evaluations and present recommendations to executives.

· Strong executive communication and stakeholder leadership.

 

Preferred Qualifications

· Experience in consumer goods, retail, or eCommerce optimization (e.g., Amazon marketplace, ad spend optimization).

· Experience with Salesforce Cloud suite and customer data platforms.

· Exposure to LLMOps frameworks, guardrail tooling, and prompt engineering.

· Familiarity with regulatory compliance and data privacy in AI systems.