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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:
Supply Chain EngineeringJob Sub Function:
Automation EngineeringJob Category:
Scientific/TechnologyAll Job Posting Locations:
Santa Clara, California, United States of AmericaJob Description:
Johnson \& Johnson RAD (Robotics and Digital) is seeking for a Staff Engineer AI \& ML – OTTAVA, for our Santa Clara, Location.
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.
About MedTech Surgery
Fueled by innovation at the intersection of biology and technology, we’re developing the next generation of smarter, less invasive, more personalized treatments.
Are you passionate about improving and expanding the possibilities of surgery? Ready to join a team that’s reimagining how we heal? Our Surgery team will give you the chance to deliver surgical technologies and solutions to surgeons and healthcare professionals around the world. Your contributions will help effectively treat some of the world’s most prevalent conditions such as obesity, cardiovascular disease and cancer. Patients are waiting.
Your unique talents will help patients on their journey to wellness. Learn more
at https://www.jnj.com/medtech
Purpose
The Staff Engineer in Analytics and AI/ML for Digital Manufacturing is dedicated to advancing data\-driven manufacturing within our supply chain operations. This role involves leading the design, development, and implementation of analytics and artificial intelligence/machine learning solutions that provide both diagnostic and predictive insights to support real\-time performance management and informed decision\-making in intelligent, compliant operations.
The successful candidate will collaborate with stakeholders across Manufacturing Engineering, Operations, Quality, J\&J Technology, and R\&D to deliver practical solutions that generate measurable business benefits and support the scaling of future operations.
Key Responsibilities
- Define technical requirements and architecture for analytics and AI/ML solutions across manufacturing environments (edge, OT, and cloud).
- Lead end\-to\-end delivery of analytics and ML solutions, including data ingestion, feature engineering, model development, validation, deployment, and lifecycle management.
- Design, implement, and operate production\-grade pipelines and inference (batch and real time), with monitoring and SLAs for latency, availability, and throughput.
- Translate manufacturing challenges (yield, downtime, quality, throughput) into measurable use cases with clear KPIs and expected ROI.
- Establish MLOps and governance practices (model versioning, experiment tracking, reproducibility, access control, audit trails) aligned to regulated manufacturing expectations (CSV, GxP where applicable).
- Partner with Manufacturing Engineering, Operations, Quality, IT, and R\&D to prioritize and scale high\-value use cases (predictive quality, anomaly detection, predictive maintenance, process optimization) and translate them into scalable analytics; ensure adoption through documentation, playbooks, training, and stakeholder engagement.
- Apply statistical methods and experimentation (e.g., DOE, SPC, capability analysis) to quantify drivers, validate improvements, and support continuous improvement.
Qualifications:
- Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Engineering, or a related quantitative field.
- 7\+ years of experience delivering analytics and/or ML solutions in production environments (manufacturing, supply chain, healthcare/MedTech, or other regulated industries preferred).
Required:
- Proven track record delivering ML/AI solutions into production at scale; experience in manufacturing or industrial/OT environments strongly preferred.
- Experience with manufacturing and industrial data sources (e.g., MES, OPC UA, PLC logs, telemetry, sensors) and translating domain requirements into deployable ML solutions.
- Strong Python skills; experience with ML libraries (e.g., scikit\-learn, TensorFlow, PyTorch) and data processing frameworks (e.g., Spark/PySpark).
- Hands\-on MLOps experience, including orchestration, CI/CD, model serving, monitoring/observability, automated retraining, and experiment tracking (e.g., MLflow).
- Proficiency in SQL and data modeling; familiarity with lakehouse/data lake patterns (e.g., Delta) and cloud data services (AWS or Azure equivalents), including secure architecture design.
- Applied expertise in time\-series and process analytics (anomaly detection, forecasting, classification/regression), including feature engineering and model interpretability/performance evaluation.
- Experience with model governance, validation, and compliance in regulated environments; familiarity with data governance, security, and role\-based access controls (CSV/GxP where applicable).
- Strong communication and stakeholder\-management skills, including the ability to document architecture/validation artifacts and present to technical and non\-technical audiences.
Preferred:
- Experience with cloud analytics and lakehouse platforms and orchestration (e.g., Databricks, Spark/Delta) and effective collaboration with data engineering teams.
- Familiarity with digital manufacturing architectures and standards (e.g., ISA\-95/ISA\-88\); unified naming/semantic standards are a plus.
- Experience with visualization and decision\-support tools (e.g., Power BI, Tableau) and building role\-appropriate dashboards.
Other:
- May require up to 10% of domestic and/or international travel
- The attendance policy required for this role is Fully Onsite
Johnson \& Johnson is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, age, national origin, disability, protected veteran status or other characteristics protected by federal, state or local law. We actively seek qualified candidates who are protected veterans and individuals with disabilities as defined under VEVRAA and Section 503 of the Rehabilitation Act.
Johnson \& Johnson is committed to providing an interview process that is inclusive of our applicants’ needs. If you are an individual with a disability and would like to request an accommodation, external applicants please contact us via https://www.jnj.com/contact\-us/careers , internal employees contact AskGS to be directed to your accommodation resource.
Required Skills:
Preferred Skills:
The anticipated base pay range for this position is :
$109,000\.00 to $153,748\.00
Additional Description for Pay Transparency:
Subject to the terms of their respective plans, employees and/or eligible dependents are eligible to participate in the following Company sponsored employee benefit programs: medical, dental, vision, life insurance, short\- and long\-term disability, business accident insurance, and group legal insurance. 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)). This position is eligible to participate in the Company’s long\-term incentive program. 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 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 Condolence Leave – 30 days for an immediate family member: 5 days for an extended family member Caregiver Leave – 10 days Volunteer Leave – 4 days Military Spouse Time\-Off – 80 hours Additional information can be found through the link below. https://www.careers.jnj.com/employee\-benefits
Salary Context
This $109K-$153K 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 ($131K) sits 21% below the category median. Disclosed range: $109K to $153K.
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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