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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.
Working at Abbott
At Abbott, you can do work that matters, grow, and learn, care for yourself and family, be your true self and live a full life. You’ll also have access to:
- Career development with an international company where you can grow the career you dream of.
- Free medical coverage for employees\* via the Health Investment Plan (HIP) PPO
- An excellent retirement savings plan with high employer contribution
- Tuition reimbursement, the Freedom 2 Save student debt program and FreeU education benefit \- an affordable and convenient path to getting a bachelor’s degree.
- A company recognized as a great place to work in dozens of countries around the world and named one of the most admired companies in the world by Fortune.
- A company that is recognized as one of the best big companies to work for as well as a best place to work for diversity, working mothers, female executives, and scientists.
The Opportunity:
We currently have an opportunity for an AI Engineer. This position works out of our Plano, TX location in the Neuromodulation Division. Our Neuromodulation business includes implantable devices compatible with mobile technology to help people who suffer from chronic pain and movement disorders. These non\-opioid therapies allow us to provide interventional pain therapy to patients throughout the pain continuum.
What You’ll Work On
- Collaborate with an interdisciplinary team, and partner closely with third\-party vendors, to integrate Abbott's core AI principles (Responsible, Safe, Fair, Impactful, and Transparent) into the core of our advanced technological products and externally sourced solutions.
- Assist in model validation activities, including performance monitoring, fairness evaluations, and explainability analyses for both internally developed and vendor\-supplied models.
- Develop and implement robust, human\-aligned AI systems by creating feedback mechanisms, from analyzing system\-level traceability to interpreting user interactions, to ensure our technology adapts and behaves exactly as intended, emphasizing system accountability and transparency.
- Support the assessment of intelligent systems for ethical considerations, specifically auditing for algorithmic bias and ensuring strict privacy protections, aligning them with human ethical values to build stakeholder trust.
- Engage with worldwide regulatory and emerging industry standards, such as the IEEE CertifAIEd criteria, and global ethical frameworks. Champion patient safety and product integrity by ensuring all development and quality processes adhere strictly to critical medical device standards, including IEC 62304, ISO 14971, ISO 13485, and 21 CFR 820\.
- Drive continuous improvement and ensure Abbott remains at the forefront of responsible AI engineering.
Required Qualifications:
- Bachelor’s degree in Computer Science, Engineering, or related field.
- 0\-2 years of related experience
- Proficiency in software development and programming languages (such as C, C\+\+, or Python) with a focus on building robust, high\-quality products.
- Strong analytical and problem\-solving skills with high attention to detail and a foundational knowledge of AI/ML data pipelines.
Preferred Qualifications:
- Experience developing automated semantic segmentation tools to quantify and classify multiple complex material classes (or biological structures) at a microscopic level. Ability to apply advanced computational and image analysis techniques to evaluate both healthy and degraded biological states, facilitating a deeper understanding of therapeutic efficacy.
- Familiar with biological principles
- Passion for innovation, continuous learning, and a deep desire to understand the ethical challenges and opportunities facing the technology and healthcare sectors today. Familiarity with ethical AI frameworks and assessment criteria, such as those from IEEE (Accountability, Transparency, Algorithmic Bias, and Privacy).
- Exposure to AI fairness analysis, decision explainability, and rigorous model performance monitoring frameworks.
- Excellent communication skills, with the ability to explain complex technical and ethical concepts to diverse, cross\-functional audiences (including external vendors and partners). Proactive and motivated to contribute to delivering impactful and life\-changing solutions.
- Participants who complete a short wellness assessment qualify for FREE coverage in our HIP PPO medical plan. Free coverage applies in the next calendar year.
Learn more about our health and wellness benefits, which provide the security to help you and your family live full lives: www.abbottbenefits.com
Follow your career aspirations to Abbott for diverse opportunities with a company that can help you build your future and live your best life. Abbott is an Equal Opportunity Employer, committed to employee diversity.
Connect with us at www.abbott.com, on Facebook at www.facebook.com/Abbott and on Twitter @AbbottNews.
The base pay for this position is $50,700\.00 – $101,300\.00\. In specific locations, the pay range may vary from the range posted.
Salary Context
This $50K-$101K range is in the lower quartile 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 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 $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 ($76K) sits 58% below the category median. Disclosed range: $50K to $101K.
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.
Abbott AI Hiring
Abbott has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Abbott Park, IL, US, Plano, TX, US. Compensation range: $101K - $198K.
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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