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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 122,000 colleagues serve people in more than 160 countries.
About Abbott
About Abbott (standard corporate boilerplate; pull the current approved version from Talent Acquisition/Brand before external use): Abbott is a global healthcare leader that helps people live more fully at all stages of life. Our portfolio spans diagnostics, medical devices, nutritionals, and branded generic medicines, and our colleagues serve people in more than 160 countries.
The Opportunity
A hands-on software engineering leadership role accountable for building production-grade agentic systems on Abbott's enterprise AI Platform. This role turns business and product requirements into reliable single- and multi-agent solutions, reusable engineering patterns, and measurable outcomes. The role is Director-mapped and begins as a player-coach: it leads delivery and technical standards while building and managing a team of agentic engineers.
What You'll Do
- Design, implement, and operate production agentic systems, including supervisor, planner/executor, and multi-agent patterns with explicit boundaries, failure handling, and human oversight.
- Build durable, resumable workflows with persistent state, checkpoints, replay, idempotency, compensation, and human-in-the-loop controls (e.g., LangGraph- or Temporal-style durable execution).
- Engineer tool and function integrations with Abbott enterprise systems, including MCP-style tool contracts, schemas, authorization, retries, timeouts, compensation, and failure containment.
- Develop context-engineering strategies covering retrieval, chunking and indexing, memory hierarchies, context-window budgeting, caching, structured outputs, and grounding.
- Create evaluation systems with curated datasets, task-success and safety metrics, LLM-as-judge and programmatic evaluators, regression gates in CI, and online, A/B, and shadow evaluation.
- Instrument agent applications through the platform's model and agent registries and tracing services so prompts, tool calls, intermediate steps, quality, cost, and latency are observable and auditable.
- Engineer for production reliability: streaming, concurrency, latency and token-cost optimization, rate limits, graceful degradation, secure deployment, SLOs, on-call, incident response, and rollback of agent regressions.
- Write and review substantial production code in Python and at least one of TypeScript, Go, or Java; establish testing, code review, API design, and secure software engineering practices.
- Lead technical discovery and delivery with AI Product Managers, Platform Engineering, Applied Data Science, security, and business stakeholders; communicate feasibility, risks, and delivery commitments.
- Hire, coach, and develop a team of approximately 3–8 agentic engineers; set goals, allocate work, review performance, and create reusable patterns that feed back into the platform paved road.
Required Qualifications
- 10–15+ years of software engineering experience, including substantial delivery of distributed, API-driven, or workflow-intensive production systems and technical leadership.
- Hands-on experience designing, coding, testing, deploying, and operating agentic or LLM-based applications in production; this role is expected to remain an active technical contributor.
- Strong understanding of agent architecture, tool calling, durable state, multi-agent coordination, context engineering, retrieval, evaluation, observability, and safety controls.
- Strong software engineering ability in Python and at least one of TypeScript, Go, or Java, including APIs, distributed systems, CI/CD, testing, code review, and secure SDLC.
- Experience defining measurable quality, safety, reliability, latency, and cost indicators and using evaluation results to improve systems through iterative releases.
- Bachelor's degree in a related field or equivalent professional experience.
- Demonstrated ability to lead engineers, make sound technical decisions under uncertainty, and communicate effectively with technical and non-technical stakeholders.
Preferred Qualifications
- Experience with agent frameworks or durable workflow systems (e.g., LangGraph, Temporal, or comparable technologies) and model-provider APIs.
- Experience in a highly regulated environment such as health tech, med-tech, or life sciences, including GxP, HIPAA, validation, auditability, and change-control constraints.
- Experience building secure tool-use systems, prompt-injection defenses, human-approval workflows, responsible-AI controls, and privacy-preserving agent experiences.
- Experience shipping reusable agent patterns, evaluation tooling, SDKs, or paved-road capabilities for multiple product or engineering teams.
The base pay for this position is $149,300.00 – $298,700.00. In specific locations, the pay range may vary from the range posted.
Salary Context
This $149K-$298K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($224K) sits 6% above the category median. Disclosed range: $149K to $298K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Abbott AI Hiring
Abbott has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Alameda, CA, US, Abbott Park, IL, US. Compensation range: $200K - $298K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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