Senior Director, AI Engineering Transformation

SharkNinja•Needham, MA

3d•$225,000 - $250,000•Onsite

About The Position

This is a high-impact leadership role at the intersection of AI, engineering, product quality, and organizational transformation. You will own the strategy and execution of AI integration across SharkNinja's Engineering organization, spanning the full development lifecycle from concept and architecture through design, validation, and production, and extending into reliability engineering, DFM/DFT, test data, and manufacturing engineering. Today, much of this work relies on PowerPoint-based program management, fragmented data, manual analysis, and institutional knowledge trapped in people's heads. You will change that. You will build the data foundation, deploy AI capabilities, and transform workflows so that SharkNinja's engineers develop products faster, smarter, and with higher quality at every stage. This role carries a dual mandate: drive the current portfolio of AI initiatives to measurable completion, and partner directly with Engineering leadership to shape what comes next. You are not here to deliver a fixed list of projects. You are here to build the AI capability that transforms how the Engineering organization works -- today and in the future. You report directly to the VP, Chief of Staff to the CEO, with a dotted line to the CTO/Chief Engineering Officer.

Requirements

10+ years of professional experience in AI/ML, engineering leadership, program management, data architecture, quality engineering, or technology transformation roles
Strong technical fluency in AI/ML and data infrastructure: you can evaluate tools, assess platforms, understand data pipelines, and hold your own in technical discussions with engineers, data scientists, and product teams
Proven track record leading large-scale transformation or change management initiatives, ideally in engineering, R&D, or consumer products environments
Deep understanding of engineering development lifecycles in a hardware or consumer electronics context, including stage-gate processes, DVT/EVT/PVT builds, and cross-functional launch execution
Proven ability to both build (hands-on implementation) and think strategically -- not just one or the other
Strong business acumen: you connect technical capabilities to business outcomes and communicate that connection to non-technical stakeholders
Exceptional leadership skills: you attract talent, build high-performing teams, and influence without authority across a matrixed organization
Decisive and action-oriented: you make decisions with imperfect information, move fast, and course-correct without hesitation
Comfortable operating in ambiguity and a high-velocity environment where priorities shift and speed matters more than perfection

Nice To Haves

Experience at an AI-native company or within an engineering organization where AI was core to the development process
Background bridging technical build teams and business stakeholders, with the ability to translate requirements in both directions
Experience productionalizing AI prototypes (moving from proof-of-concept to enterprise-grade tools with proper back-end infrastructure)
Familiarity with engineering quality concepts: FMEAs, DVT, EMC/regulatory compliance, reliability testing, field-return analytics
Ability to speak Mandarin

Responsibilities

Architect and implement a unified data infrastructure across Engineering, resolving the fragmented, siloed data landscape that is the #1 bottleneck to AI adoption
Map data assets across the organization (schematics, CAD/PCB design files, test results, FMEAs, DVT/EMC results, lessons learned, field return data, quality metrics) and build the ingestion pipelines that make this data usable by AI systems
Partner with IT and data engineering to connect Engineering's data infrastructure to the enterprise data architecture (Snowflake, AWS)
Define and execute the AI transformation roadmap across the full engineering lifecycle, while continuously identifying new AI opportunities with Engineering leadership as capabilities evolve
Lead the reimagination of program management infrastructure, replacing manual, PowerPoint-based workflows with AI-powered tooling that enables real-time program status visibility, automated accountability, and permission-based dashboards
Deploy AI-powered planning intelligence by ingesting historical engineering data to improve forecasting accuracy and reduce late-stage surprises
Transform reliability and quality capabilities, failure prediction, DVT/EMC outcome analysis, field-return pattern detection at scale and partner with Engineering leadership to define what the next generation of AI-powered engineering quality looks like
Reimagine design and test workflows by making lessons learned, test results, and FMEAs accessible through AI, so past engineering knowledge automatically informs future product development
Strengthen Engineering's contribution to product requirements by building AI systems that surface relevant historical data (prior test failures, field issues, quality patterns) during the design and requirements process
Build, lead, and scale a team of AI Fellows embedded directly into Engineering teams to drive hands-on AI adoption
Drive change management across large, global engineering teams with varying levels of AI fluency, with particular focus on director-level and below adoption
Translate complex AI capabilities into practical, adoptable solutions that engineers, program managers, and cross-functional teams actually use
Establish success metrics, track adoption, and report measurable outcomes to executive leadership
Champion a culture of experimentation: fast iteration, learning from failure, and scaling what works