Interested in this AI/ML Engineer role at Edwards Lifesciences?
Apply Now →About This Role
Innovation starts from the heart. At Edwards Lifesciences, we’re dedicated to developing ground\-breaking technologies with a genuine impact on patients’ lives. At the core of this commitment is our investment in cutting\-edge information technology. This supports our innovation and collaboration on a global scale, enabling our diverse teams to optimize both efficiency and success. As part of our IT team, your expertise and commitment will help facilitate our patient\-focused mission by developing and enhancing technological solutions.
We're seeking a Technical Product Owner, AI/ML with a strong software engineering and AI systems background to serve as the primary AI Engineering liaison between business and technical teams. This role translates complex business needs into scalable, secure, high\-value AI\-enabled solutions while ensuring disciplined execution and measurable outcomes.
How you will make an impact:
- Provide senior technical product leadership across a product capability or portfolio spanning complex technical products and systems (e.g., software platforms and hardware\-enabled solutions)
- Define product vision, operating standards, and governance to enable scalable delivery across products and pods
- Align senior stakeholders on priorities and partner with engineering and architecture leaders to drive execution
- Guide prioritization and integration of AI/ML use cases into scalable, production\-ready product capabilities
- Ensure measurable delivery, adoption, and value for data\-enabled and intelligent products (e.g., analytics, automation, AI/ML\-enabled capabilities)
- Partner closely with business stakeholders, data scientists, AI engineers, and platform teams to shape product strategy
- Define product requirements and evaluate value, feasibility, and ROI for AI/ML initiatives
- Deliver high\-quality AI products on time through strong program execution and cross\-functional coordination
- Apply deep technical product thinking and fluency to drive scalable, impactful solutions
- Act as the primary AI Engineering liaison for multiple business functions
- Communicate AI capabilities, constraints, and trade\-offs clearly to stakeholders
- Conduct value proposition, feasibility, and ROI assessments
- Lead requirements gathering workshops with stakeholders
- Facilitate business process mapping and future\-state design
- Own and manage the product backlog aligned to business objectives
- Ensure alignment with governance, security, and responsible AI standards across products and platforms
- Partner with Lead Engineers to ensure technical soundness
- Develop PRDs, functional and non\-functional requirements
- Contribute to system design and architectural discussions
- Validate technical designs for scalability, reliability, and maintainability
- Stay current with AI, cloud, and engineering best practices
- Demonstrate understanding of distributed systems and AI/ML workflows
- Drive end\-to\-end delivery from ideation to launch
- Create and manage program and project plans
- Coordinate across engineering, data science, and platform teams
- Track risks, dependencies, and delivery status
- Support product adoption and post\-launch evaluation
What you’ll need (Required):
- Bachelor’s or Master’s degree in Computer Science or related field with 8 years in software engineering, technical product, or program roles
- Experience delivering AI, data, or platform\-based products.
What Else We Look For (Preferred):
- Deep technical fluency, strong product thinking, and program execution excellence
- Demonstrate strong software engineering and system design fundamentals
- Apply understanding of AI/ML workflows, model lifecycle, and deployment considerations
- Leverage experience with APIs, microservices, and cloud\-native architectures
- Communicate effectively across business and technical domains to drive alignment
- Experience collaborating with business stakeholders, data scientists, AI engineers, and platform teams to shape strategy, define requirements, evaluate value and ROI, and deliver high\-quality AI products on time
- Experience with AI/ML platforms, MLOps, or generative AI solutions
- Experience operating within regulated industries
- Familiarity with Agile, Scrum, or SAFe frameworks
What Success Looks Like
- Strong alignment between AI initiatives and business priorities, resulting in measurable value realization
- Clear, high\-quality, and actionable requirements that enable efficient engineering execution
- Consistent delivery of scalable, high\-impact AI products on committed timelines
- Trusted partnership across business and technical stakeholders
Aligning our overall business objectives with performance, we offer competitive salaries, performance\-based incentives, and a wide variety of benefits programs to address the diverse individual needs of our employees and their families.
For California, the base pay range for this position is $121,000 to $171,000 (highly experienced).
The pay for the successful candidate will depend on various factors (e.g., qualifications, education, prior experience). Applications will be accepted while this position is posted on our Careers website.
E dwards is an Equal Opportunity/Affirmative Action employer including protected Veterans and individuals with disabilities.
COVID Vaccination Requirement
Edwards is committed to protecting our vulnerable patients and the healthcare providers who are treating them. As such, all patient\-facing and in\-hospital positions require COVID\-19 vaccination. If hired into a covered role, as a condition of employment, you will be required to submit proof that you have been vaccinated for COVID\-19, unless you request and are granted a medical or religious accommodation for exemption from the vaccination requirement. This vaccination requirement does not apply in locations where it is prohibited by law to impose vaccination.
Salary Context
This $121K-$171K range is below the median 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 Edwards Lifesciences, 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 ($146K) sits 19% below the category median. Disclosed range: $121K to $171K.
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.
Edwards Lifesciences AI Hiring
Edwards Lifesciences has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Irvine, CA, US, Alton, IL, US. Compensation range: $153K - $171K.
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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