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We are the people who give possibilities purpose
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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.
Job Description
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We are seeking a highly experienced Senior Staff AI/ML Algorithm Engineer to help advance next\-generation medical technologies for continuous physiological monitoring, disease detection, and predictive clinical decision support.
In this role, you will develop clinically meaningful algorithms using multi\-modal healthcare data, including physiological sensor signals, medical device data, electronic health records, imaging data, and other real\-world clinical datasets. You will work in a highly cross\-functional environment with algorithm engineers, data scientists, clinicians, systems engineers, software teams, regulatory specialists, and product leaders to translate complex clinical and physiological problems into validated, deployable algorithmic solutions.
This is a senior technical role suited for an engineer with deep expertise in classical machine learning, modern AI/deep learning, physiological signal interpretation, and real\-world deployment across both cloud\-based platforms and edge medical devices.
Key Responsibilities
- Lead the design, development, validation and deployment of AI/ML algorithms for continuous physiological monitoring, novel vital sign and physiological parameter development, disease detection and risk stratification and early warning systems.
- Develop algorithms using multi\-modal data sources, including physiological waveforms and time\-series data such as PPG, blood pressure, capnography and other monitoring signals, bedside and wearable sensor data, electronic health records and structured clinical data, etc.
- Apply advanced analytical approaches, including:
- Classical machine learning models
- Statistical modeling and probabilistic inference
- Time\-series modeling
- Signal processing and feature engineering
- Deep learning architectures
- Multimodal AI models
- Transformer\-based models and foundation\-model approaches where appropriate
- Translate clinical and physiological understanding into robust algorithm design, including feature selection, model architecture, performance targets, and clinically interpretable outputs.
- Build end\-to\-end algorithm development pipelines for data ingestion, signal quality assessment, annotation, feature extraction, model training, validation, robustness testing, and post\-market performance monitoring where applicable.
- Partner with clinical, medical affairs, regulatory, quality, and product teams to ensure algorithms are developed with appropriate clinical context, usability, safety, and regulatory considerations.
- Develop and optimize algorithms for cloud environments and for edge or embedded medical devices, accounting for latency, memory, compute, power, cybersecurity, and reliability constraints.
- Support verification, validation, documentation, risk analysis, and design controls for regulated medical technology products.
- Contribute to intellectual property generation, technical strategy, research roadmaps, and external scientific engagement.
- Mentor engineers and data scientists, provide technical leadership, and help establish best practices for healthcare AI algorithm development.
Required Qualifications
- 10\+ years of industry experience in AI/ML, algorithm development, biomedical signal processing, healthcare analytics, medical devices, or related technical areas.
- Master’s or PhD degree preferred in Electrical Engineering, Biomedical Engineering, Computer Science, Data Science, Applied Mathematics, Statistics, Computational Biology, or a related engineering or quantitative field.
- Deep expertise in machine learning and AI model development, including both classical and modern approaches.
- Strong hands\-on experience with physiological time\-series data, biomedical signals, medical device data, or clinical datasets.
- Solid understanding of human physiology, especially as it relates to patient monitoring, cardiopulmonary function, hemodynamics, respiratory status, neurological monitoring, or acute care settings.
- Demonstrated experience developing algorithms from early feasibility through productization or deployment.
- Experience working with multi\-modal healthcare data, such as sensor signals, EHR data, imaging data, lab data, and clinical outcomes.
- Strong programming skills in Python and experience with ML/AI frameworks such as PyTorch, TensorFlow, scikit\-learn, or equivalent tools.
- Experience with cloud\-based algorithm development and deployment using platforms such as Azure, AWS, or GCP.
- Experience optimizing models for deployment on constrained environments, including embedded systems, edge devices, or near\-real\-time medical technology platforms.
- Strong understanding of model evaluation, including sensitivity, specificity, AUROC, calibration, false alarm burden, clinical utility, robustness, and generalizability.
- Excellent communication skills with the ability to explain complex technical concepts to engineering, clinical, regulatory, and business stakeholders.
Preferred Qualifications
- Experience developing AI/ML algorithms for regulated medical devices, digital health products, clinical decision support tools, or Software as a Medical Device (SaMD).
- Familiarity with medical device development practices, including design controls, risk management, verification and validation, human factors, clinical performance evaluation, and regulatory documentation.
- Experience with relevant standards and regulatory frameworks such as FDA expectations for AI/ML\-enabled medical devices.
- Experience with signal quality assessment, artifact detection, missing\-data handling, sensor fusion, uncertainty quantification, and explainable AI.
- Familiarity with MLOps, model lifecycle management, version control, data governance, automated testing, and post\-deployment monitoring.
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?
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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 healthcare. At BD, you’ll discover a culture in which you can learn, grow and thrive.
We believe that when people connect in person, we learn faster, collaborate more deeply, and build a stronger culture. Join us and enjoy a culture where face\-to\-face collaboration supports your learning, your progress, and your success.
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 CA \- Irvine Laguna CanyonAdditional Locations
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Work Shift
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At BD, we reward, support and develop our associates through our comprehensive Total Rewards program. We are committed to attracting and retaining high quality talent by providing reward and recognition opportunities that promote a performance\-based culture, as well as a competitive package of compensation and benefits programs. You can learn more on our career site under "Our Commitment to You."
Our salary or hourly rate ranges reward associates fairly and competitively. We regularly review these ranges and factors, such as location, contribute to the range displayed.
Our pay is based on the role and the necessary skills and education to perform it successfully. The salary or hourly rate offered is determined by the role's specific requirements, including any applicable step rate pay system at the work location. Salary or hourly pay ranges are influenced by labor laws and Collective Bargaining Agreement (CBA) requirements applicable to the work location which may also affect the workplace arrangement of the role.
Salary Range Information
$146,000\.00 \- $233,600\.00 USD Annual
Salary Context
This $146K-$233K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($189K) sits 12% below the category median. Disclosed range: $146K to $233K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
BD AI Hiring
BD has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Vernon Hills, IL, US, Laguna Canyon, CA, US. Compensation range: $207K - $307K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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