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About This Role
Job ID R\-542052 Date posted 07 April 2026
Job Description Summary
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Job Description
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We are the makers of possible
BD is one of the largest global medical technology companies in the world. Advancing the world of health™ is our Purpose, and it’s no small feat. It takes the imagination and passion of all of us—from design and engineering to the manufacturing and marketing of our billions of MedTech products per year—to look at the impossible and find transformative solutions that turn dreams into possibilities.
We believe that the human element, across our global teams, is what allows us to continually evolve. Join us and discover an environment in which you’ll be supported to learn, grow and become your best self. Become a maker of possible with us.
Position Summary
The ISC Project Engineer serves as theOperationsrepresentative on platform‑led sustaining engineering projects. This role acts as theprimary liaison between manufacturing sites and platform project teams, translating project requirements into executable site actions. The position emphasizes coordination, communication, andexecutionreadiness to support on‑time delivery, manage competing priorities, and reduce operational risk.
Key Responsibilities
- Serve as a core team member on sustaining engineering projects, ensuring site and operations requirements are incorporated into scope, risk assessments, validation strategies, and implementation plans.
- Act as the primary point of contact between platform project teams and manufacturing sites, communicating execution needs, risks, constraints, and status.
- Provide site‑level input on capacity, timing, and resource availability to support project sequencing and prioritization decisions.
- Coordinate site execution activities, including validations, process studies, engineering work orders, material trials, and line‑time planning.
- Monitor site readiness and execution risks, escalatingimpactsand constraints to project leadership and the PMO to enabletimelymitigation.
- Support implementation of sustaining changes such as material or packaging updates, process changes, compliance initiatives, and business continuity projects.
- Ensure completion of required documentation and change control activities for site‑impacting changes, including validation documentation, training coordination, andimplementationcloseout.
Required Qualifications
- Bachelor’s degree in Engineering, Business Administration, Supply Chain, Project Management, Communications, ora related field.
- 3\+years of experience in manufacturing, sustaining engineering, process engineering, or product lifecycle support within a regulated industry.
- Working knowledge of validation, change control, risk management, and manufacturing documentation.
- Experience supporting cross‑functional project teams with site execution responsibilities.
- Strong communicationand coordination skills, with the ability to influence without direct authority.
- Experienceoperatingin a quality‑ and regulatory‑controlled environment.
Preferred Qualifications
- Experience in medical devices or another highly regulated manufacturing environment.
- Exposure to cost‑out, supply continuity, compliance, or end‑of‑life initiatives.
- Experience coordinating site trials or execution across multiple manufacturing lines or sites.
- Familiarity with SAP manufacturing and document control systems.
- PMP, Lean Six Sigma, or similar project or operations certifications.
- Business analysis, product management, or negotiation/mediation training.
Success in This Role
- Manufacturing sites have a clear, trusted point of contact for sustaining engineering initiatives.
- Project teams haveaccuratevisibility into site capacity, timing, and execution constraints.
- Validations, trials, and implementations are planned earlier and executed with fewer disruptions.
- Sustaining projects progress efficiently through strong coordination between sites and platform teams.
- Site sustaining needs are clearly communicated and effectively prioritized at the platform level.
At BD, we prioritize on\-site collaboration because we believe it fosters creativity, innovation, and effective problem\-solving, which are essential in the fast\-paced healthcare industry. For most roles, we require a minimum of 4 days of in\-office presence per week to maintain our culture of excellence and ensure smooth operations, while also recognizing the importance of flexibility and work\-life balance. Remote or field\-based positions will have different workplace arrangements which will be indicated in the job posting.
For certain roles at BD, employment is contingent upon the Company’s receipt of sufficient proof that you are fully vaccinated against COVID\-19\. In some locations, testing for COVID\-19 may be available and/or required. Consistent with BD’s Workplace Accommodations Policy, requests for accommodation will be considered pursuant to applicable law.
Why Join Us?
A career at BD means being part of a team that values your opinions and contributions and that encourages you to bring your authentic self to work. It’s also a place where we help each other be great, we do what’s right, we hold each other accountable, and learn and improve every day.
To find purpose in the possibilities, we need people who can see the bigger picture, who understand the human story that underpins everything we do. We welcome people with the imagination and drive to help us reinvent the future of health. At BD, you’ll discover a culture in which you can learn, grow, and thrive. And find satisfaction in doing your part to make the world a better place.
To learn more about BD visit https://bd.com/careers
Becton, Dickinson, and Company is an Equal Opportunity Employer. We evaluate applicants without regard to race, color, religion, age, sex, creed, national origin, ancestry, citizenship status, marital or domestic or civil union status, familial status, affectional or sexual orientation, gender identity or expression, genetics, disability, military eligibility or veteran status, and other legally\-protected characteristics.
Required Skills
Optional Skills
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Primary Work Location
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USA NJ \- Franklin LakesAdditional Locations
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Work Shift
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At BD, we are strongly committed to investing in our associates—their well\-being and development, and in providing rewards and recognition opportunities that promote a performance\-based culture. We demonstrate this commitment by offering a valuable, competitive package of compensation and benefits programs which you can learn more about on our Careers Site under Our Commitment to You.
Salary or hourly rate ranges have been implemented to reward associates fairly and competitively, as well as to support recognition of associates’ progress, ranging from entry level to experts in their field, and talent mobility. There are many factors, such as location, that contribute to the range displayed. The salary or hourly rate offered to a successful candidate is based on experience, education, skills, and any step rate pay system of the actual work location, as applicable to the role or position. Salary or hourly pay ranges may vary for Field\-based and Remote roles.
Salary Range Information
$91,600\.00 \- $151,100\.00 USD Annual
Salary Context
This $91K-$151K 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 BD, 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. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($121K) sits 27% below the category median. Disclosed range: $91K to $151K.
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.
BD AI Hiring
BD has 7 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Franklin Lakes, NJ, US, Tempe, AZ, US, El Paso, TX, US. Compensation range: $151K - $326K.
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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