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About This Role
At Johnson \& Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity. Learn more at jnj.com
As guided by Our Credo, Johnson \& Johnson is responsible to our employees who work with us throughout the world. We provide an inclusive work environment where each person is considered as an individual. At Johnson \& Johnson, we respect the diversity and dignity of our employees and recognize their merit.
Job Function:
Data Analytics \& Computational SciencesJob Sub Function:
Data ScienceJob Category:
Scientific/TechnologyAll Job Posting Locations:
Raritan, New Jersey, United States of AmericaJob Description:
To maintain our \#1 Healthcare supply chain serving our patients, customers and parents – and remaining agile in the new digital world, J\&J is looking to build capability in developing supply chain solutions that enable supporting the above initiatives.
The role requires both an in\-depth knowledge of existing advanced analytics practices and approaches including Machine Learning, Mathematical Optimization, Simulation, data pipelines and advanced visualization and the ability to influence and convince others and customize and work in a multidisciplinary environment to drive business solutions. Knowledge of application of advanced analytics to manufacturing and especially pharmaceutical / healthcare / medical device manufacturing is highly desirable.
The candidate will report to manager of J\&J SC Data Science to:
- Lead Options Modeling initiatives across a portfolio of brands, supporting strategic and tactical decisions related to capacity, network design, resiliency, cost, and service.
- Design, build, and maintain simulation\-based digital twins, incorporating demand forecasts, capacity constraints, policies, and operational variability.
- Gather business requirements from business partners and accurately translate those into digital \& data science solution prototypes involving predictive analytics/simulation components, validate the prototypes and work closely with product development teams to scale the prototypes
- Partner with business stakeholders to frame decision questions, define scenarios, and interpret model results into clear, actionable insights.
- Collaborate with business analysts, engineers, IT, and data teams to ensure that prototypes are scaled optimally with respect to adherence to business requirements, solution cost and agility to flex the solutions
- Assist digital product managers in new digital product launches i.e. ensuring that the right user tests are designed for product validation, develop metrics and product analytics to monitor and optimize products
- Assist digital product managers in sustaining and optimizing existing digital \& data science products and platforms. This would involve continuous improvements to already deployed models by testing new generations of data science models, evaluating optimal ways to implement those models (e.g. open source vs vendor sourced analytic components)
- undefined
- Monitor external trends on new types of modeling approaches \& solution capabilities to continuously improve deployed digital solutions in targeted supply chain areas such as value chain management, planning, customer analytics, supplier risk management etc.
Qualifications
- Master’s degree in Industrial Engineering, Operations Research, Systems Engineering, Data Science, or a related field with 5\+ years of relevant experience, OR
PhD in a quantitative or engineering discipline with hands\-on applied modeling experience.
- Strong expertise in Simio and discrete\-event simulation for complex operational systems.
- Strong expertise in Python libraries such as numpy, scipy, pandas, scikit\-learn, pyomo, simpy etc.. Proven experience demonstrating deployment of models built using python libraries in production environment
- Must have expertise in various types of Operations Research models E.g. Optimization models (Linear Programming, Non\-Linear Programming, Mixed Integer Programming), Simulation models (Discrete Event Models, Monte Carlo Simulations Etc.), parameter estimation models (e.g. Markov Chain Monte Carlo Etc.)
- Solid understanding of supply chain concepts, such as capacity planning, manufacturing flows, network design, inventory, and service trade\-offs. Experience with modeling supply chain management particularly in the healthcare industry (pharmaceuticals/medical devices) is highly desired.
- Must have demonstrated competence in working with business partners to translate business requirements into digital solution features and data science models
- Must have experience with deployment of open source based models into production within cloud based digital stacks. Candidates must have demonstrated the ability to work with data engineers to leverage data pipelines for operationalizing models.
- Familiarity with Azure or AWS Cloud components such as Databricks and associated big data frameworks such as Spark, PySpark etc.
- Must have intermediate to high degree of expertise in tableau or similar visualization tools to created prototypes of dashboards for validation and insights delivery
- Familiarity and experience with ERP systems like SAP including unstructured data sources is preferred
Required Skills:
Preferred Skills:
Advanced Analytics, Business Intelligence (BI), Coaching, Collaborating, Critical Thinking, Data Analysis, Database Management, Data Privacy Standards, Data Reporting, Data Savvy, Data Science, Data Visualization, Econometric Models, Process Improvements, Technical Credibility, Technologically Savvy, Workflow AnalysisThe anticipated base pay range for this position is :
$109,000\.00 \- $174,800\.00
Additional Description for Pay Transparency:
Subject to the terms of their respective plans, employees are eligible to participate in the Company’s consolidated retirement plan (pension) and savings plan (401(k)).
Subject to the terms of their respective policies and date of hire, employees are eligible for the following time off benefits:
Vacation –120 hours per calendar year
Sick time \- 40 hours per calendar year; for employees who reside in the State of Colorado –48 hours per calendar year; for employees who reside in the State of Washington –56 hours per calendar year
Holiday pay, including Floating Holidays –13 days per calendar year
Work, Personal and Family Time \- up to 40 hours per calendar year
Parental Leave – 480 hours within one year of the birth/adoption/foster care of a child
Bereavement Leave – 240 hours for an immediate family member: 40 hours for an extended family member per calendar year
Caregiver Leave – 80 hours in a 52\-week rolling period10 days
Volunteer Leave – 32 hours per calendar year
Military Spouse Time\-Off – 80 hours per calendar year
Salary Context
This $109K-$174K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Johnson & Johnson, 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 $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($141K) sits 15% below the category median. Disclosed range: $109K to $174K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Johnson & Johnson AI Hiring
Johnson & Johnson has 29 open AI roles right now. They're hiring across AI/ML Engineer. Positions span San Antonio, TX, US, Spring House, PA, US, Santa Clara, CA, US. Compensation range: $106K - $401K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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