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About This Role
Job ID R\-540681 Date posted 31 March 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.
Reporting to the SVP, Medical Affairs for BD Interventional Business Units, the Director of Medical Affairs, Vascular Platform provides strategic medical and scientific leadership for the BD Peripheral Intervention (BDPI) Vascular portfolio. The Director serves as the primary medical and scientific lead for the Vascular platform, accountable for medical strategy and execution across innovation, new product development, diligence and integration activities, lifecycle evidence strategy, and global clinical support. Working in close partnership with Platform Leaders, the Director ensures alignment of medical strategy with business priorities and serves as a member of both the Platform Leadership Team and the Innovation Council.
Responsibilities
- Provide strategic medical and clinical leadership for innovation and new product development, including concept ideation, cross‑functional collaboration, and definition of clinical evidence requirements for regulatory approval, safety, and efficacy.
- Define and guide global pre‑market and post‑market clinical evidence strategies to support registration, reimbursement, and market adoption.
- Lead strategic KOL engagement, including oversight of advisory boards, ensuring high‑quality scientific exchange, actionable insights for cross‑functional teams, and full compliance with regulatory and ethical standards.
- Support long‑term business growth through medical due diligence for mergers, acquisitions, partnerships, and integrations; identify and communicate medical risks and implications to senior leadership.
- In partnership with Medical Safety leadership, oversee medical safety and risk management for the Vascular portfolio, ensuring compliance with global regulatory requirements (e.g., FDA, PMDA, Notified Bodies, GLPs) and providing clinical expertise for adverse event assessment, post‑market surveillance, risk management planning, safety communications, and Field Action Committee activities.
- Contribute to the Innovation Council by identifying emerging technologies, assessing unmet clinical needs, and evaluating new product concepts.
- Review and provide medical input on Investigator‑Initiated Study (IIS) proposals for business consideration; oversee or coordinate preclinical studies as required.
- Lead and develop a high‑performing Medical Affairs team, ensuring appropriate training, coaching, and performance management across roles, including global Medical Affairs teams and Medical Science Liaisons.
- Engage early and continuously with cross‑functional partners, including Marketing, Regulatory, R\&D, Clinical Affairs, Quality, Business Development, Scientific Affairs, Physician Training, and Global Medical Affairs Regions to align medical strategy with program and platform objectives.
- Partner with Marketing to assess the scientific and clinical validity of product claims and ensure medical accuracy and promotional compliance.
- Collaborate with Clinical Affairs on clinical trial design, execution, analysis, and interpretation, including endpoint selection and patient population strategy.
- Shape publication and presentation strategies in partnership with Scientific Affairs to maximize scientific credibility and business impact.
- Support regulatory submissions, labeling development, medical information responses, and ad hoc medical expertise requests, including Legal support, ensuring scientific rigor and compliance.
Qualifications:
- Medical Doctor (MD or DO) degree from an accredited institution required.
- Clinical training or practice experience in Vascular Surgery, Endovascular Medicine, Interventional Cardiology, or Interventional Radiology strongly preferred.
- Minimum 8\+ years of progressive scientific and medical leadership experience, with demonstrated authority and credibility in medical decision‑making.
- Experience in the medical device industry preferred, particularly supporting device‑based vascular therapies and peripheral vascular interventions.
- Prior experience in a Medical Affairs leadership role or equivalent, with a proven ability to engage, influence, and align senior internal and external stakeholders.
- Demonstrated expertise in clinical evidence generation across the product lifecycle, including pre‑market and post‑market activities.
- Experience with medical device safety, risk management, and post‑market surveillance, with the ability to clinical data to inform strategy and business decisions.
- Strong knowledge of global medical device regulatory pathways and clinical evidence requirements, including FDA (510(k), PMA), CE Mark, and post‑market obligations.
- Inclusive, values‑based leadership style with the ability to influence, negotiate, and shape organizational culture.
- Proven people leadership capabilities, including the ability to develop talent, lead through change, and drive aligned execution across teams.
- Excellent communication skills, with the ability to distill complex scientific and clinical information into clear, actionable insights.
- Strong relationship‑building skills, fostering trust and collaboration with cross‑functional partners and external stakeholders.
- High adaptability to evolving platform needs, diverse perspectives, and dynamic business environments.
- Deep understanding of ethical, compliance, and governance standards within Medical Affairs.
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.
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. 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.
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. You will work alongside inspirational leaders and colleagues who are equally passionate and committed to fostering an inclusive, growth\-centered, and rewarding culture. You will have the opportunity to help shape the trajectory of BD while leaving a legacy at the same time.
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.
Becton, Dickinson and Company is an Equal Opportunity/Affirmative Action Employer. We do not unlawfully discriminate on the basis of 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, or any other protected status.
Required Skills
Optional Skills
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Primary Work Location
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USA AZ \- Tempe HeadquartersAdditional Locations
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USA IL \- Vernon Hills, USA MA \- Lexington, USA MO \- St Louis, USA TX \- Austin, USA WA \- SeattleWork 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
$181,400\.00 \- $326,500\.00 USD Annual
Salary Context
This $181K-$326K range is above the 75th percentile 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 ($253K) sits 52% above the category median. Disclosed range: $181K to $326K.
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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