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About This Role
Abbott is a global healthcare leader that helps people live more fully at all stages of life. Our portfolio of life\-changing technologies spans the spectrum of healthcare, with leading businesses and products in diagnostics, medical devices, nutritionals and branded generic medicines. Our 115,000 colleagues serve people in more than 160 countries.
Position Overview
Abbott Vascular is seeking a Staff AI/ML Engineer to develop advanced machine learning solutions for optical coherence tomography (OCT)–based intravascular imaging systems. This role will focus on developing robust, clinically relevant algorithms to automatically identify and characterize key anatomical and interventional features. The successful candidate will translate cutting\-edge AI research into scalable, high\-performance solutions integrated into regulated medical device products. The role requires close collaboration with clinical, imaging, software, and systems engineering teams throughout the product development lifecycle.Key Responsibilities* Design, develop and deploy machine learning and deep learning models for clinical applications including image segmentation, feature detection, classification and quantitative analysis of OCT images.
- Lead the development of advanced semantic segmentation algorithms for OCT\-based vascular imaging applications.
- Own end\-to\-end ML lifecycle, including data preparation, annotation strategies, model development, training, validation, performance optimization, and deployment.
- Collaborate with clinical and V\&V teams to define clinically meaningful outputs, evaluation metrics, and validation methodologies.
- Develop algorithms capable of robust performance across diverse patient populations, imaging conditions, and clinical use cases.
- Optimize models for real\-time or near\-real\-time inference in embedded or product environments.
- Evaluate and integrate state\-of\-the\-art machine learning approaches, including foundation models, self\-supervised learning, and generative AI techniques where appropriate.
- Contribute to software architecture, code reviews, and engineering best practices to ensure scalable and maintainable AI solutions.
- Generate technical documentation supporting design controls, verification, validation, risk management, and regulatory submissions.
Required Qualifications* Master’s or Ph.D. in Computer Science, Electrical Engineering, Biomedical Engineering, or a related field with a focus on AI/ML.
- 7\+ years of industry experience developing machine learning, deep learning, or computer vision solutions, or 4\+ years of relevant industry experience with a PhD.
- Strong expertise in deep learning architectures and modern machine learning approaches for image analysis, including convolutional neural networks and vision transformers
- Proven experience developing semantic segmentation, object detection, classification, and image analysis algorithms.
- Strong programming skills in Python and experience with ML frameworks such as PyTorch, TensorFlow, or equivalent.
- Experience designing experiments, evaluating model performance, and applying statistical methods for model validation.
- Demonstrated ability to translate research concepts into prototype and production\-quality software solutions.
- Excellent communication and cross\-functional communication skills.
Preferred Qualifications* Experience with OCT, IVUS, CT, MRI or other medical imaging modalities, including multi\-modal image registration techniques.
- Understanding of OCT imaging principles, image formation physics, and common imaging artifacts.
- Experience developing AI/ML solutions for 3D volumetric, temporal, longitudinal or sequential imaging datasets.
- Experience with foundation models, generative AI, self\-supervised learning, multimodal learning, or other advanced AI approaches applied to medical imaging.
- Expertise in model deployment and optimization for real\-time or near\-real\-time inference using technologies such as ONNX, TensorRT, TorchScript, or equivalent frameworks.
- Experience establishing scalable MLOps workflows, including data and model versioning, experiment tracking, continuous integration/deployment, and production ML pipelines.
- Familiarity with medical device software development processes in regulated environments, including FDA regulations, design controls, V\&V, and AI/ML\-related FDA guidelines.
- Demonstrated ability to translate research\-grade algorithms into robust, production\-ready solutions for clinical or commercial applications.
The base pay for this position is $99,300\.00 – $198,700\.00\. In specific locations, the pay range may vary from the range posted.
Salary Context
This $99K-$198K range is below the median 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 Abbott, 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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($149K) sits 32% below the category median. Disclosed range: $99K to $198K.
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.
Abbott AI Hiring
Abbott has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Santa Clara, CA, US, Alameda, CA, US, Westford, MA, US. Compensation range: $198K - $344K.
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
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