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About This Role
At Edwards Lifesciences, the Advanced Innovation & Technology (AI&T) teams harness imagination, courage, and resourcefulness to think beyond what’s currently possible and create solutions for patients many years into the future.
As part of AI&T, we're building an AI Center of Excellence (COE) focused on applying the power of AI to help patients live longer, healthier lives. As we expand our focus across the structural heart disease journey, AI is creating new opportunities to better understand and improve patient care. Our teams are applying AI to support clinical decision-making, identify patients earlier, develop digital solutions that complement our therapies, and help employees work more efficiently so they can focus on work that matters most. We're building an AI COE that collaborates across our innovation-focused teams throughout our global organization, helping accelerate innovation and build AI capabilities that create lasting impact for patients, clinicians, and employees. Together, we're committed to applying AI responsibly and thoughtfully while keeping patients at the center of everything we do.
This is a rare opportunity to help define how AI is applied across the structural heart disease journey while shaping the next generation of therapies, digital solutions, and patient experiences. We’ll give you the tools and resources you need to create groundbreaking innovations that shape the future of structural heart technology.
How you will make an impact:
As a Principal Scientist, you will help define the scientific and technical direction of AI across the structural heart disease journey. This role requires deep expertise in machine learning and scientific leadership, with the ability to evaluate new technologies, incubate novel capabilities, and guide teams developing AI-enabled products in regulated healthcare environments.
- Lead the design and development of scalable AI solutions that address complex business problems in partnership with business subject matter experts, product managers, data scientists, and engineering teams.
- Lead the development of domain-specific foundation models across structured and unstructured clinical and procedural datasets, including natural language, medical imaging, and waveforms.
- Drive research and experimentation in generative AI, multimodal AI, representation learning, self-supervised learning, agentic systems, and advanced machine learning techniques.
- Ensure models are developed with appropriate rigor for generalizability, safety, fairness, robustness, and real-world performance.
- Identify, evaluate, and incubate new technology opportunities, including simulation, computational modeling, digital twins, physics-informed AI, scientific machine learning, and next-generation foundation models.
- Deliver written quarterly technology-scouting positions that inform real investment decisions.
- Set scientific standards for the broader science group and provide technical leadership and mentorship to junior engineers and data scientists.
- Partner with engineering, quality, clinical, and regulatory teams to support development of AI-enabled products and digital solutions.
- Collaborate on model evaluation, validation, risk management, and deployment strategies for regulated healthcare applications.
- Assess operational feasibility by evaluating problem definitions, analyses, requirements, solution development, and proposed solutions.
- Build and maintain robust data pipelines to train, test, and evaluate large language models (LLMs) and traditional machine learning models; engineer solutions using AI/ML frameworks and libraries.
- Stay current with generative AI and traditional AI/ML research and trends and incorporate relevant advances into product offerings.
- Improve and optimize model performance to ensure robustness, scalability, and efficiency.
- Partner closely with AI Evaluation, Quality, Regulatory Affairs, and Software Engineering teams to support verification, validation, risk assessment, and regulatory documentation activities.
- Rigorously evaluate models for systemic bias and stand up MLOps frameworks to monitor performance drift or shifts across different datasets.
- Ensure code and model development are well documented and follow Edwards engineering practices.
What you’ll need (Required):
- Bachelor’s degree plus 6 years of experience in AI/ML engineering, with demonstrated success working with cross-functional IT and/or business teams on enterprise-level, complex, or novel system implementations.
- Proven experience designing, training, and validating machine learning models end to end.
- Deep technical breadth across at least two of the following: natural language processing/agentic AI, computer vision, time-series and signal modeling, or computational modeling and simulation.
What else we look for (Preferred):
- MS or PhD (preferred, but not required) in computer science, machine learning, biomedical engineering, or a related field, or equivalent industry depth.
- 10 to 15 years in applied machine learning or AI research, with a track record of shipping models into production, not just publications.
- Proficiency with common ML and DL libraries (e.g., scikit-learn, NumPy, SciPy, PyTorch, TensorFlow/Keras), NLP/LLM libraries and APIs (e.g., Hugging Face libraries, LangChain or LlamaIndex, frontier model APIs, cloud and data platform APIs), and computer vision libraries (e.g., OpenCV, scikit-image, PIL, torchvision).
- Senior individual-contributor technical leadership: provide influence on technical direction and provide mentoring without direct people leadership.
- Prior experience in a cardiovascular, structural heart, or broader medtech environment.
- Experience deploying AI/ML models to production systems, not just research models.
- Experience in a leadership or senior technical role driving AI/ML strategy.
- Experience with modern deep learning model architectures, such as convolutional neural networks (including U-Nets), transformers (including large language models and vision transformers), diffusion models, and graph neural networks.
- Experience with learning paradigms and system designs, such as generative AI, foundation models, self-supervised learning (e.g., masked autoencoders and contrastive learning), multimodal learning, retrieval-augmented generation, agentic systems, and physics-informed machine learning.
- Experience with cloud computing environments (particularly Amazon Web Services and Microsoft Azure), as well as remote and distributed computing.
- Strong technical programming skills, particularly in Python; experience with R, SQL, Julia, Rust, HTML, JavaScript, CSS, Java, C++, or similar languages is also valued.
- Experience working with data platforms, including Snowflake, Databricks, and/or Palantir.
- Experience supporting development of regulated software, Software as a Medical Device (SaMD), or AI-enabled medical devices.
- Familiarity with design controls, verification and validation activities, risk management, and Good Machine Learning Practice (GMLP).
- Experience with version control systems such as Git and agile development methodologies.
- A peer-reviewed publication record or equivalent recognized technical standing.
- Relevant experience and training in the technologies used for development.
- Excellent analytical, organizational, time management, information-seeking, and attention to detail skills.
- Excellent written and verbal communication, negotiation, interpersonal, and relationship management skills, with the ability to drive achievement of objectives.
- Ability to manage multiple tasks, competing priorities, and long-term goals in a fast-paced environment.
- Ability to interact professionally with colleagues at all organizational levels.
- Ability to follow all company requirements, including applicable health and safety protocols, and take appropriate measures to prevent injury, protect the environment, and prevent pollution within the scope of the role.
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 (CA), the base pay range for this position is $132,000 to $186,000 (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.
Edwards is an Equal Opportunity/Affirmative Action employer including protected Veterans and individuals with disabilities.
Salary Context
This $132K-$186K range is below the median 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 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 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($159K) sits 25% below the category median. Disclosed range: $132K to $186K.
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.
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, Irvine, CA. Compensation range: $186K - $186K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 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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