Interested in this AI/ML Engineer role at Smith+Nephew?
Apply Now →About This Role
Life Unlimited. At Smith\+Nephew we design and manufacture technology that takes the limits off living. The Head of Product, AI is responsible for defining and executing the enterprise AI product strategy, transforming AI into a durable competitive advantage across the company’s portfolio and internal product development ecosystem.
What will you be doing?
This leader owns the end‑to‑end lifecycle of AI‑enabled products, platforms, and capabilities—ensuring AI is systematically designed, built, tested, deployed, and scaled to deliver differentiated customer value and measurable productivity gains. Beyond customer‑facing features, the role plays a critical leadership function in embedding AI across product development, R\&D, and engineering workflows, shaping how products are conceived, delivered, and evolved. This is a highly cross‑functional role requiring deep cross\-functional collaboration including NPD Engineering, Early Innovation teams, and IT leaders, as well as strong influence across business units.
AI Product Features \& Platforms (80%) across the enterprise
Own the AI product portfolio, including embedded AI features, AI services, and internal platforms.
Define requirements and roadmaps for AI capabilities that deliver differentiated customer value.
Partner with research and engineering to move models from experimentation to production at scale.
Establish reusable AI components (models, APIs, tooling) to reduce duplication across teams.
AI\-Enabled New Product Development (NPD) (10%)
Design and lead an AI first NPD framework that accelerates:
Customer discovery and insights
Concept generation and prioritization
Design and prototyping
Development and testing
Launch readiness and post\-launch learning
Identify opportunities to apply AI across the NPD lifecycle, including:
Market and customer insight generation
Requirements synthesis and backlog creation
Code generation, test automation, and simulation
Experimentation, A/B testing, and feedback analysis
Standardize AI workflows, tools, and best practices across product and R\&D teams.
Enterprise Enablement, Governance \& Change Leadership (10%)
Act as the enterprise product owner for AI in R\&D, driving adoption across business units.
Partner with Product, Engineering, Design, Data, and IT leaders to embed AI into daily workflows.
Define success metrics for AI\-driven productivity, speed, and quality improvements.
Location: Pittsburgh, PA or Andover, MA
Education: Master’s or Ph.D. in Computer Science, Computer Engineering, or a related technical field preferred, BA required.
What will you need to be successful?
10\+ years of product leadership experience, preferably in R\&D‑intensive, platform‑driven, or technology‑first organizations.
Demonstrated success delivering AI‑powered products and platforms from concept through scale.
Strong background working with R\&D, or applied research teams, with the ability to translate technical capabilities into product value.
Proven ability to influence without direct authority in complex, matrixed organizations.
Preferred Experience:
Experience as a senior leader as Sr. Director/VP
Experience driving AI transformation of internal workflows, not just customer features.
Familiarity with developer tooling, and AI assisted software development.
Experience operating in regulated or IP sensitive environments.
Travel Requirements: 25%
You Unlimited.
- The anticipated base compensation range for this position is $254,000 \- $362,875 USD annually. The actual base pay offered to the successful candidate will be based on multiple factors, including but not limited to job\-related knowledge/skills, experience, and geographic location. Compensation decisions are dependent upon the facts and circumstances of each position and candidate. In addition to base pay, we offer competitive bonus and benefits, including medical, dental, and vision coverage, 401(k), tuition reimbursement, medical leave programs, parental leave, generous PTO, paid company holidays, 8 hours of volunteer time annually, and a variety of wellness offerings such as EAP.
- Inclusion \+ Belonging: Committed to Welcoming, Celebrating and Thriving. Learn more about our Employee Inclusion Groups on our website https://www.smith\-nephew.com/
- Your Future: 401k Matching Program, 401k Plus Program, Discounted Stock Options, Tuition Reimbursement
- Work/Life Balance: Flexible Personal/Vacation Time Off, Paid Holidays, Flex Holidays, Paid Community Service Day
- Your Wellbeing: Medical, Dental, Vision, Health Savings Account (Employer Contribution of $500\+ annually), Employee Assistance Program, Parental Leave, Fertility and Adoption Assistance Program
- Flexibility: Hybrid Work Model (For most professional roles)
- Training: Hands\-On, Team\-Customized, Mentorship
- Extra Perks: Discounts on fitness clubs, travel and more!
Smith\+Nephew provides equal employment opportunities to applicants and employees without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, or disability.
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Salary Context
This $254K-$362K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Smith+Nephew, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($308K) sits 70% above the category median. Disclosed range: $254K to $362K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Smith+Nephew AI Hiring
Smith+Nephew has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Andover, MA, US. Compensation range: $362K - $362K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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