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About This Role
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 4,100 associates, the company’s products are sold at key retailers, online and offline, and through distributors around the world.
AI at SharkNinja
At SharkNinja, we’re building an AI\-native culture. We’re not waiting for the future; we’re creating it. Our people are expected to experiment boldly, adopt new tools, and continuously raise what’s possible to create meaningful impact for our consumers. If you believe the best way to do your job hasn’t been invented yet, you’ll fit right in.
Sr. Director, AI Engineering Transformation
Location: Boston, MA preferred; New York, NY acceptable (On\-site)
Reports to: VP, Chief of Staff to the CEO
Dotted Line: Chief Technology Officer / Chief Engineering Officer
Direct Reports: AI Transformation Team (AI Fellows)
The Role
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.
What You Will Do
Data Foundation \& Infrastructure
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)
Engineering Transformation
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
Team Building \& Change Management
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
Who You Are
Required
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
Strongly Preferred
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
Additional Requirements
Travel required (domestic and international, including China manufacturing sites)
On\-site at Boston HQ or New York office full\-time
Why This Role Matters
SharkNinja's CEO has made AI transformation a top company priority. This role sits at the center of a company\-wide movement to fundamentally change how SharkNinja's Engineering organization designs, builds, tests, and ensures the quality of its products. You will have direct visibility and support from the highest levels of the organization.
SharkNinja's Engineering organization is entering a pivotal chapter: new category expansion, a connected device ecosystem, and a global portfolio that demands faster, smarter execution with higher quality at every stage. You will be in the trenches with engineering teams, building the data foundation, proving what works, scaling it, and making AI an irreversible part of how this company engineers and delivers best\-in\-class products
Salary and Other Compensation: The annual salary range for this position is displayed below. Factors which may affect starting pay within this range may include geography/market, skills, education, experience and other qualifications of the successful candidate.
The Company offers the following benefits for this position, subject to applicable eligibility requirements: medical insurance, dental insurance, vision insurance, flexible spending accounts, health savings accounts (HSA) with company contribution, 401(k) retirement plan with matching, employee stock purchase program, life insurance, AD\&D, short\-term disability insurance, long\-term disability insurance, generous paid time off, company holidays, parental leave, identity theft protection, pet insurance, pre\-paid legal insurance, back\-up child and eldercare days, product discounts, referral bonus program, and more.
Pay Range $225,000 — $250,000 USD
Our Culture
At SharkNinja, we don’t just raise the bar—we push past it every single day. Our Outrageously Extraordinary mindset drives us to tackle the impossible, push boundaries, and deliver results that others only dream of. If you thrive on breaking out of your swim lane, you’ll be right at home.
What We Offer
We offer competitive health insurance, retirement plans, paid time off, employee stock purchase options, wellness programs, SharkNinja product discounts, and more. We empower your personal and professional growth with high impact Learning Programs featuring bold voices redefining what’s possible. When you join, you’re not just part of a company—you’re part of an outrageously extraordinary community. To gether, we won’t just launch products— we’ll disrupt entire markets.
At SharkNinja, Diversity, Equity, and Inclusion are vital to our global success. Valuing each unique voice and blending all of our diverse skills strengthens SharkNinja’s innovation every day. We support ALL associates in bringing their authentic selves to work, making an impact, and having the opportunity for career acceleration. With help from our leadership, associates, and our community, we aim to have equity be a key component of the SharkNinja DNA.
Learn more about us:
Life At SharkNinja
Outrageously Extraordinary
SharkNinja Candidate Privacy Notice
For candidates based in all regions , please refer to this Candidate Privacy Notice .
For candidates based in China , please refer to this Candidate Privacy Notice .
For candidates based in Vietnam , please refer to this Candidate Privacy Notice .
We do not discriminate on the basis of race, religion, color, national origin, sex, gender, gender expression, sexual orientation, age, marital status, veteran status, disability, or any other class protected by legislation, and local law. SharkNinja will consider reasonable accommodations consistent with legislation, and local law. If you require a reasonable accommodation to participate in the job application or interview process, please contact SharkNinja People \& Culture at [email protected]
Salary Context
This $225K-$250K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At SharkNinja, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($237K) sits 9% above the category median. Disclosed range: $225K to $250K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
SharkNinja AI Hiring
SharkNinja has 7 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Needham, MA, US, US. Compensation range: $90K - $275K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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