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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.
Applied AI \& Analytics Associate, Office of the CEO
3 Open Roles \| Miami, FL \| Office of the CEO — Data, Analytics \& AI
You Don't Wait to Be Asked. Neither Do We.
At SharkNinja, we've built a culture around one simple belief: the best answer wins. Not the loudest opinion. Not the most senior voice in the room. The best answer — backed by data, delivered with clarity, and executed with urgency.
We're hiring three Applied AI \& Analytics Associates to sit inside the Office of the CEO. This is not a support role. It is not a reporting role. It is a problem\-solving role — one where you'll work directly alongside the VP, Chief of Staff to the CEO and senior leaders across the company to crack open the problems that matter most and turn them into decisions that drive the business forward.
If you thrive in ambiguity, build before you're told to, and believe that a clean insight delivered fast beats a perfect deck delivered late — read on.
What You'll Actually Do
This team runs point on high\-impact, cross\-functional priorities across the company. You'll be handed incomplete questions — and expected to turn them into complete answers.
On any given week, you might:
Receive a vague but urgent business question from a senior leader and independently scope, pull, clean, and analyze the data to produce a clear recommendation — fast
Build a lightweight dashboard, model, or workflow that a team can actually use (not just admire)
Identify a blind spot in how a team is measuring success and fix it before it becomes a problem
Use AI tools to accelerate your own analysis — and help others do the same
Present findings to a cross\-functional audience and make a complex topic land simply
Connect the dots between data from multiple teams to surface something no single team could see on their own
Spot a broken process or a data gap and propose a better way, without waiting for permission
This is a hands\-on, high\-ownership role. You will move fast, iterate often, and be expected to deliver outcomes — not activity.
What It Takes to Win Here
Must Have:
1–2\+ years of experience in analytics, data science, consulting, or a fast\-paced, data\-driven environment
Strong, practical command of SQL, Python, or comparable tools — you work with data directly, not through intermediaries
Demonstrated ability to take an ambiguous problem, structure it, and deliver a clear answer
Experience building dashboards or analytical tools (Tableau, Power BI, Looker, or similar)
Exceptional communication skills — you can explain what the data says to both engineers and executives in the same meeting
A track record of ownership: you follow through, you close the loop, and you make things happen
Even Better If You Have:
Experience deploying AI or automation tools in a real work context (not just familiarity — actual usage)
Background in consumer products, retail, or e\-commerce — you understand how these businesses work
Experience operating in proximity to senior leadership on high\-stakes projects
Comfort with experimentation, A/B testing, or applied statistics
Experience wrangling large, messy, or poorly documented datasets — and making sense of them anyway
Tools \& Stack
Languages: SQL, Python (or equivalent)
BI \& Visualization: Tableau, Power BI, Looker, or similar
Data Platforms: Experience with modern cloud data platforms is a plus (Snowflake, BigQuery, Databricks, etc.)
AI Tools: Hands\-on experience with AI\-assisted workflows is strongly preferred
Why This Role, Why Now
SharkNinja is one of the fastest\-growing consumer product companies in the world. We operate with the intensity of a startup and the scale of a global brand — and the Office of the CEO is where the most important, cross\-cutting work gets done.
This role gives you direct exposure to how a company at this scale makes decisions, solves hard problems, and keeps moving. You will not be on the sidelines. You will be in the room.
If you want a role where you can see the impact of your work — where you're not just producing output but shaping outcomes — this is it.
Equal Opportunity Employer
SharkNinja is committed to building a diverse, equitable, and inclusive workplace. We provide equal employment opportunities to all individuals regardless of background, identity, or experience, and are committed to providing reasonable accommodations throughout the hiring process and beyond.
We believe diverse teams build better products. We'd love to hear from you.
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 $111,600 — $136,400 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 accommodations@sharkninja.com
Salary Context
This $111K-$136K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% 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 $166,983 based on 13,781 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $76,880. This role's midpoint ($124K) sits 26% below the category median. Disclosed range: $111K to $136K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
SharkNinja AI Hiring
SharkNinja has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Miami, FL, US. Compensation range: $136K - $136K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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