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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.
Head of AI and Data Platform
Lingo Division – Abbott
Location: Alameda, CA
Working at Abbott
At Abbott, you can do work that matters, grow, and learn, care for yourself and your 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.
- Employees can qualify for free medical coverage in our Health Investment Plan (HIP) PPO medical plan in the next calendar year.
- An excellent retirement savings plan with a 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 worldwide 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 the best place to work for diversity, working mothers, female executives, and scientists.
About Lingo
Meet Lingo, a new biosensing technology that provides users a window into their body. Lingo tracks key biomarkers such as glucose, ketones, and lactate to help people make better decisions about their health and nutrition. Biowearable technology will digitize, decentralize and democratize healthcare, enabling consumers to take control of their own health.
THE OPPORTUNITY
At Lingo, we are building a groundbreaking health platform that combines continuous biosensor data, real\-time analytics, and personalized insights to help people live fuller, longer, and healthier lives. Our systems ingest millions of sensor readings daily, powering AI\-driven experiences for consumers and partners worldwide, with the reliability and scalability of cloud\-native, enterprise\-grade platforms.
We are seeking an experienced and hands\-on Head of AI and Data Platform to define the technical vision and lead the engineering execution of Lingo's AI, machine learning, and data infrastructure. This is a leadership role for a builder who has personally architected and delivered production AI and data systems at scale, brings deep technical instincts across ML engineering, data platform design, and LLM\-based product development, and knows how to grow and galvanize engineering teams to ship innovative AI\-powered health products in a regulated, fast\-moving environment.
This is not a strategy\-only role. You will be deeply involved in system design, architecture decisions, and critical technical problem solving as a contributor, not just a reviewer, while also building and leading a high\-performing global team of AI and data engineers.
What You'll Work On
- Define and own the technical architecture for Lingo's AI and data platform, including biosensor data ingestion pipelines, real\-time and batch processing infrastructure, feature stores, model serving layers, and data lake design.
- Drive the architecture and engineering execution of AI\-powered product features, including personalized metabolic health insights, predictive analytics from CGM data, LLM\-based conversational health experiences, and on\-device ML inference from biosensor data.
- Establish engineering standards for AI system design, including model integration patterns, RAG pipeline architecture, prompt engineering practices, evaluation and observability frameworks, and responsible AI guardrails appropriate for a regulated health context.
- Lead the engineering execution of ML model development, training, evaluation, and deployment pipelines, ensuring models reach production reliably, safely, and with the observability required to detect drift and degradation.
- Build and own MLOps infrastructure including experiment tracking, model registry, automated retraining pipelines, A/B testing frameworks, and model monitoring for production AI systems.
- Partner with Data Science and Product teams to translate model research and product requirements into scalable, production\-ready AI systems that perform reliably at consumer scale.
- Define standards for LLM integration, including prompt management, retrieval\-augmented generation, evaluation harnesses, latency and cost optimization, and safety guardrails for health\-related conversational AI.
- Ensure AI and ML systems maintain ongoing alignment with regulatory requirements including HIPAA, GDPR, and FDA software guidance, in close partnership with Regulatory Affairs, Legal, and Quality Assurance.
- Define, manage, and report on engineering OKRs, KPIs, and delivery metrics for the AI and Data Platform function, presenting progress and insights to senior stakeholders.
- Standardize tools, development processes, and data engineering practices across AI and data squads to improve alignment, data quality, and delivery consistency.
Required Qualifications:
- Bachelor's degree in computer science, Engineering, Mathematics, or a related technical discipline. Advanced degree in Machine Learning, Data Science, or equivalent preferred.
- 15\+ years of progressive experience in software and data engineering, with a strong foundation as an individual contributor who has personally built production AI and data systems at scale before moving into leadership.
- Proven hands\-on experience architecting and building large\-scale data platforms, including real\-time streaming pipelines, data lakes, feature stores, and ML serving infrastructure on cloud platforms (Azure, AWS, or GCP).
- Deep system design expertise in distributed data systems: you can whiteboard and lead the design of event\-driven data architectures, data models, caching strategies, and real\-time biosensor data pipelines from first principles.
- Meaningful hands\-on experience engineering AI\-powered products, including integrating LLMs into production systems, building RAG or agentic pipelines, and deploying ML models within consumer\-facing applications.
- Strong understanding of AI and ML system design considerations, including model serving infrastructure, latency and cost trade\-offs, evaluation frameworks, observability, data feedback loops, and safety constraints in sensitive health domains.
- Exceptional executive communication skills, including the ability to translate AI and data complexity into clear product and business narratives for C\-suite and board audiences.
Preferred Qualifications
- Background in digital health, consumer health technology, or other highly regulated industries such as HIPAA, GDPR, and FDA oversight.
- Hands\-on experience with IoT or biosensor data ingestion pipelines, real\-time analytics, or wearable device platforms.
- Experience applying AI and ML in health or wellness contexts, including personalization engines, anomaly detection on sensor data, or clinically informed recommendation systems.
- Experience with IEC 62304\-based software development processes for medical device software.
- Experience in high\-growth scale\-up or venture\-backed environments with exposure to rapid product and organizational scaling.
- Strong experience defining OKRs, KPIs, and capacity planning for high\-throughput, consumer\-facing AI platforms with high\-availability requirements.
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.
The base pay for this position is $172,000\.00 – $344,000\.00\. In specific locations, the pay range may vary from the range posted.
Salary Context
This $172K-$344K range is above the 75th percentile 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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($258K) sits 18% above the category median. Disclosed range: $172K to $344K.
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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