Interested in this AI/ML Engineer role at Zimmer Biomet?
Apply Now →About This Role
At Zimmer Biomet, we believe in pushing the boundaries of innovation and driving our mission forward. As a global medical technology leader for nearly 100 years, a patient’s mobility is enhanced by a Zimmer Biomet product or technology every 8 seconds.
As a Zimmer Biomet team member, you will share in our commitment to providing mobility and renewed life to people around the world. To support our talent team, we focus on development opportunities, robust employee resource groups (ERGs), a flexible working environment, location specific competitive total rewards, wellness incentives and a culture of recognition and performance awards. We are committed to creating an environment where every team member feels included, respected, empowered and recognised.
What You Can Expect
Reporting to a Vice President, the Director, AI Product - Front Office leads the AI product strategy, portfolio, and delivery roadmap across the Commercial and Marketing teams of Zimmer Biomet. This role identifies high-value AI use cases, redesigns legacy workflows, drives enterprise adoption, and enables workforce upskilling across corporate functions. The role combines product leadership, workflow redesign, change leadership, and practical AI implementation.
The successful candidate will be AI-native: someone who uses AI directly in their own work while setting direction for AI-enabled products, agents, and workflows that support corporate functions. The Director will lead a team of 4-6 product managers and/or product professionals, and will operate across business, technical, data, risk, and governance stakeholders to deliver enterprise-scale AI outcomes.
How You'll Create Impact
- Own and govern the corporate-functions AI product portfolio, setting prioritization criteria and making portfolio sequencing, resourcing, and investment recommendations across Human Resources, Finance, Information Technology, Legal, Compliance, and IR
- Lead, develop, and retain a high-performing team of 4-6 product managers and/or product professionals, setting clear priorities, allocating resources, coaching for growth, and fostering an inclusive, accountable, and high-talent culture
- Partner with Engineering and other delivery teams in a matrix environment to align product vision, technical feasibility, capacity, delivery plans, and quality outcomes
- Lead cross-functional assessment and redesign of corporate workflows, including tasks, decision points, handoffs, controls, and regulatory or policy requirements, to define target-state AI-enabled workflows
- Set and approve product strategy, target outcomes, roadmaps, requirements, implementation plans, and measurable success criteria within the corporate-functions AI portfolio
- + Authorize progression of corporate-function agents, copilots, and AI workflow solutions through product-stage gates, subject to applicable enterprise architecture, data, security, legal, compliance, and responsible AI governance requirements
- Drive enterprise adoption through senior stakeholder engagement, communications, training, and change leadership across corporate functions
- Enable teams and leaders to build practical, responsible, and effective AI capability within their functions
- Establish portfolio measures for usage, workflow impact, productivity improvement, risk and control effectiveness, and business-value realization
- Chair senior cross-functional portfolio reviews; resolve priorities, dependencies, and delivery trade-offs within delegated authority; and escalate material investment, enterprise-risk, policy, or strategic decisions to Vice President or applicable governance forum
What Makes You Stand Out
- Uses AI to improve personal productivity, quality, planning, analysis, and communication
- Understands how to move from experimentation to disciplined business implementation
- Can design human-plus-AI workflows rather than simply recommend tools
- Can credibly coach leaders and teams because they actively use AI in their own day-to-day work
- Ability to assess and redesign cross-functional corporate workflows in a structured, business-oriented way
- Influencing senior, cross-functional stakeholders and driving alignment, decisions, and execution
- Leading adoption, training, and organizational change for new tools or workflows
- Strong executive communication, facilitation, and structured problem-solving skills
- Comfort operating in a hands-on leadership role that spans strategy through execution
- Demonstrated leadership of multiple AI products, AI-enabled workflows, or enterprise AI solutions from prioritization through implementation, adoption, and measurable value creation.
Your Background
- Bachelor’s degree in business, technology, engineering, data/analytics, or a related discipline; an advanced degree (for example, MBA, master’s in technology, data, or a related field)
- Ten or more years of progressively responsible experience in product management, AI product leadership, digital transformation, process improvement, consulting, or business transformation, including at least three years leading product managers and/or product professionals
- Experience leading and developing a team of product managers and/or product professionals, and partnering effectively with Engineering and delivery teams in a matrix environment
- Experience assessing and redesigning complex, cross-functional workflows in a structured, business-oriented way, including appropriate controls and policy or regulatory considerations
- Demonstrated ability to influence senior, cross-functional stakeholders; drive alignment, decisions, and execution; and lead enterprise adoption, training, and organizational change for new tools or workflows
- Strong executive communication, facilitation, structured problem-solving, and sound business judgment. Operating in a hands-on role that spans strategy through execution
- Bachelor's Degree and 8 years of relevant experience, or Associate's Degree and 10 years of relevant experience, or High School Diploma or Equivalent and 12 years of relevant experience AMER
Travel Expectations
- Role is remote but 15%-20% travel may be expected when working with internal clients
- Expected compensation for the role is $205,000 - 256,000 base salary per year plus performance bonuses.
EOE/M/F/Vet/Disability
Salary Context
This $205K-$256K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Zimmer Biomet, 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 $212,000 based on 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275. This role's midpoint ($230K) sits 9% above the category median. Disclosed range: $205K to $256K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Zimmer Biomet AI Hiring
Zimmer Biomet has 3 open AI roles right now. They're hiring across AI/ML Engineer, MLOps Engineer. Positions span Remote, US, Bengaluru, Karnataka, India. Compensation range: $256K - $256K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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