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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.
Senior Manager, AI \& Organizational Transformation
Supporting AI \& Org Transformation \| Product Development
About the Role
SharkNinja is integrating AI across Product Development and Engineering as a structural shift in how teams work, design, and deliver. This is not a pilot program. It is a fundamental change to how work gets done, and it requires a people strategy built specifically for AI\-era organizations.
As a newly created role, the Senior Manager, AI \& Organizational Transformation will lead that transformation. The Senior Manager, AI \& Organizational Transformation is the embedded workforce strategy leader for Product Development \& Engineering, focused on translating AI capability into practical implications for roles, workflows, job architecture, and operating models. The work spans AI workforce planning, org design, work redesign, and change enablement.
This is not a Human Resources Business Partner (HRBP) role. The focus is on how AI changes work, teams, and structure, not day\-to\-day business partner support. The right Senior Manager, AI \& Organizational Transformation brings structure to ambiguity, earns credibility with technical leaders, and delivers recommendations that drive decisions.
What You'll Own
AI Workforce Strategy \& Transformation Planning
Define the people strategy for AI adoption across Product Development \& Engineering, covering workforce implications, role evolution, capability gaps, and adoption sequencing
Build and maintain an AI transformation roadmap from a workforce perspective, aligned to SharkNinja's enterprise AI initiative and commercial priorities
Monitor GenAI and agentic AI trends and translate them into actionable workforce strategy: which roles are affected, in what timeframe, and what SharkNinja should do about it
Develop KPIs and measurement frameworks to track AI adoption, workforce readiness, productivity impact, and organizational effectiveness. Present findings to Product Development (PD) and Human Resources (HR) leadership
Org Design \& Job Architecture
Lead org design analysis and role redesign efforts tied to AI adoption across Product Development \& Engineering, in partnership with functional leaders
Apply task\-level decomposition to determine which work should remain human\-led, be AI\-augmented, be automated, or require human\-AI handoffs, and design job architectures accordingly
Define emerging role types needed for AI\-enabled work: AI workflow leads, human\-AI teaming roles, AI adoption champions, and governance\-oriented positions that don't yet exist in SharkNinja's job architecture
Ensure job architecture, competency frameworks, and career paths reflect AI\-era skills and connect to downstream processes
Workflow \& Work Redesign
Identify high\-priority Product Development \& Engineering workflows affected by AI tools and lead redesign efforts in partnership with functional process owners
Map current\-state workflows, surface automation opportunities and human\-AI handoff points, and design future\-state processes that are implementable
Build a repeatable workflow redesign methodology that HRBPs and functional leaders can use to scale this work across teams and product categories
Apply structured process improvement approaches and establish feedback loops to measure impact over time
Change Enablement \& Adoption
Lead change planning for AI workforce initiatives across PD\&E, including stakeholder engagement, communications strategy, and adoption sequencing
Develop change strategies that account for workforce anxiety, role evolution, and the cultural dynamics of an engineering and design\-heavy population
Serve as a trusted advisor to senior PD\&E leaders on the human side of AI adoption: what it means for their teams, their org structures, and their talent
Equip leaders with messaging, decision frameworks, and adoption support materials
People \& Culture (P\&C) Partnership \& Stakeholder Leadership
Focus on AI workforce transformation, not a generalist HRBP. Work closely with the Senior HRBP to align AI workforce work with the broader people agenda
Partner with Talent Acquisition, Talent Management, and Total Rewards to connect AI\-driven job design outputs to hiring, performance, and compensation frameworks
Collaborate with SharkNinja's enterprise AI initiative leads to ensure PD\&E workforce strategy is integrated, not siloed
What You Bring
Required
Bachelor's degree in Business, Organizational Development, Information Systems, Data Analytics, Computer Science, Engineering, or a related field
5\+ years of progressive experience in people strategy, org design, workforce transformation, or business transformation · Experience with AI and automation platforms such as Microsoft Copilot, Copilot Studio, Power Automate, or ChatGPT Enterprise, sufficient to engage credibly in automation design conversations
Prior consulting or internal transformation experience in AI workforce transformation, org design, or digital transformation
At least 3 years working directly in or alongside Product Development, Engineering, R\&D, Data, AI, or another technical function
Demonstrated experience leading or contributing to org design, job architecture, or role redesign efforts in the context of a major AI, digital, or operational transformation
Expert working knowledge of AI, GenAI, and agentic AI concepts, sufficient to engage credibly with engineering leaders and translate AI capability into workforce strategy without relying on technical translation from others
Experience in workflow mapping, task analysis, or business process improvement, including identifying automation opportunities and designing human\-AI handoff points
Strong analytical, executive communication, and stakeholder management skills. Able to prepare and present to VP and C\-suite audiences with minimal direction
Ability to operate in a fast\-moving, matrixed environment where priorities shift and ambiguity is the norm
Preferred
Master's degree in Information Systems, Human\-Computer Interaction, or Business Administration
Experience in a consumer products, hardware, or technology company with high SKU complexity and compressed innovation cycles
Familiarity with org design methodologies such as Galbraith Star Model or Kates Kesler, and digital org design tools such as OrgVue
Familiarity with task decomposition or work design methodologies, including tools such as Gloat, Reejig, or Mercer Work Design
What It Takes to Succeed
This role sits at the intersection of people strategy, AI transformation, org design, and work redesign. It will not be filled by someone who needs a playbook, because one does not exist yet.
The right person brings structure to ambiguity, earns credibility quickly with engineering leaders who are skeptical of HR, and delivers work that drives decisions rather than describes them. They understand AI well enough to have a real point of view on how it changes work, not just a surface\-level familiarity.
SharkNinja moves fast. Product Development \& Engineering teams ship products across 30\+ categories on compressed cycles, organizational structures evolve with the business, and People \& Culture (P\&C) is expected to be ahead of problems, not responding to them. If that pace energizes you, this is the right environment.
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 $160,000 — $190,000 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 $160K-$190K range is below the median 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 in Demand for This Role
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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($175K) sits 20% below the category median. Disclosed range: $160K to $190K.
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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