Interested in this AI/ML Engineer role at SharkNinja?
Apply Now →Skills & Technologies
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 Finance \& Legal Transformation
The Role
You will own the strategy and execution of AI integration across SharkNinja's finance organization (FP\&A, accounting, internal audit, tax, investor relations) and legal organization (contract management, IP, regulatory compliance, litigation support).
Multiple AI projects are already in flight across both functions. The challenge is not ambition \-\- it is that these efforts run in parallel without a single person connecting them, sequencing them, and driving them to completion. You will be that person: the dedicated, full\-time owner who sits above individual projects, connects the dots, and surfaces the opportunities the teams are too close to see.
You will be measured on adoption, speed, and measurable business impact. You report directly to the VP, Chief of Staff to the CEO, with a dotted line to the CFO and CLO.
What You Will Do
Strategy \& Integration
Own the AI transformation roadmap across the full finance and legal functions, identifying overlaps, dependencies, and consolidation opportunities
Sequence and prioritize ruthlessly: drive each initiative to completion before starting the next
Build and lead a team of AI Fellows embedded directly into finance and legal teams
Finance
Accelerate forecast automation, consolidating commercial forecasting across Americas, Global, and EMEA into a single AI\-powered path from prototype to production
Partner with Internal Audit to fast\-track AI adoption in audit and controls, reducing remediation timelines on material weaknesses
Activate AI opportunities across accounting \-\- from automating data entry and reconciliation to larger process redesign
Legal
Accelerate AI\-powered contract review, IP portfolio management, and regulatory compliance monitoring to reduce cycle times, outside counsel dependency, and manual exposure tracking
Build legal intelligence and knowledge retrieval systems that make institutional legal knowledge accessible and actionable across the organization
Evaluate the legal tech landscape and make build\-vs\-buy decisions that compound in value \-\- prioritizing platforms SharkNinja owns over vendor lock\-in
Drive production readiness for Legal's existing AI prototypes, moving working tools from proof\-of\-concept to enterprise\-grade platforms
Execution
Translate AI capabilities into practical solutions that finance and legal professionals actually use
Drive behavioral change across teams with varying levels of AI fluency
Establish success metrics and report measurable outcomes to executive leadership
Coordinate with external partners (PwC, Oracle) to avoid duplication and ensure AI initiatives complement existing efforts
Who You Are
Required
10\+ years in AI/ML, finance transformation, legal operations, or technology\-driven change management
Strong technical fluency in AI/ML, with hands\-on experience deploying enterprise LLM platforms (Claude, ChatGPT): you can evaluate tools, assess platforms, and hold your own in technical discussions with engineers and data scientists
Deep understanding of enterprise finance operations (FP\&A, accounting, audit, controls, ERP systems) and working knowledge of corporate legal operations (contract lifecycle, IP, compliance)
Proven track record leading large\-scale transformation in complex, multi\-stakeholder environments
Ability to bridge technical and business: you can tell IT what to build and tell the CFO or CLO what outcome it delivers
Decisive, action\-oriented, and comfortable in ambiguity where speed matters more than perfection
Experience productionalizing AI prototypes from proof\-of\-concept to enterprise\-grade tools with proper back\-end infrastructure, particularly in environments where customized platforms are already in flight
Strongly Preferred
Experience deploying AI across finance or legal at scale, not just pilots
Familiarity with SOX compliance, material weakness remediation, CLM platforms, or e\-discovery tools
Background in Legal Engineering, Forward Deployed Engineering (FDE), or comparable roles that combine production\-grade software development with domain\-specific legal workflow design
Additional Requirements
Travel required (domestic and international)
On\-site at Boston HQ in Needham, MA or New York / Miami office full\-time
Why This Role Matters
SharkNinja's CEO has made AI transformation a top company priority. Finance and Legal are where that transformation becomes measurable: faster forecasting cycles, fewer manual touches, stronger controls, faster contract turnaround, and capacity redirected to the work that matters most. Multiple projects are already underway. What is missing is the person who owns the full picture, drives each one to the finish line, and makes sure the pieces add up to more than the sum of their parts.
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 — $275,000 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-$275K 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 ($250K) sits 14% above the category median. Disclosed range: $225K to $275K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.