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About This Role
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We are the people who give possibilities purpose
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
Summary:
As a Staff Machine Learning Engineer, you will lead the design, development, and deployment of machine learning solutions that power AI-driven decision-making across the BD Connected Care portfolio. You will build and operationalize models for forecasting, optimization, anomaly detection, and other advanced analytics use cases, transforming high-quality data into scalable, intelligent products.
This is a hands-on technical leadership role where you will own the end-to-end machine learning lifecycle, from experimentation and model development to deployment, monitoring, and continuous improvement. You will help define the architecture, engineering standards, and best practices that shape the future of BD's AI platform while collaborating across engineering, product, and clinical teams to deliver impactful solutions.
The ideal candidate combines deep machine learning expertise with strong software engineering skills, takes ownership of complex technical challenges, and is motivated by BD's mission to improve patient outcomes through innovation and AI.
Key Responsibilities:
- Modeling Ownership: Own the design, training, and validation of demand forecasting, optimization, and anomaly detection models across product, operational, and clinical-adjacent data. Build probabilistic, multi-series models that emit prediction intervals rather than point estimates, so recommendations can be set to a defined service-level target.
- *Methods Breadth:*Apply classical methods (ARIMA/ETS/state-space, Bayesian, gradient boosting, hierarchical forecasting, LP/MIP/convex optimization) and deep learning approaches (transformer-based and other neural forecasting architectures, foundation-model adaptation), choosing based on the problem.
- *Training and Experimentation:*Own training pipelines end to end, including feature engineering, dataset versioning, and reproducibility. Build rigorous experimentation with temporally aware splits, honest baselines against the incumbent rule-based system, ablations, calibration, and uncertainty quantification. Design back tests and shadow-mode evaluations that precede any production trust.
- *Production:*Turn models into production services on AWS (EKS, S3, Lambda, EventBridge, Step Functions): inference services, batch jobs, and data integrations. Implement clean, modular, well-tested code with type hints, unit and integration tests, dependency hygiene, and reproducible builds.
- Evaluation and Monitoring: Implement model evaluation and continuous-evaluation harnesses, instrument production inference for quality, latency, and drift, and partner with ML Platform on packaging, deployment, promotion gates, and rollback.
- Explainability: Develop explainable outputs that surface why a recommendation was made, so users can accept, override, or investigate with confidence.
- *Mentorship and Standards:*Mentor AI and ML engineers, review designs and code, and contribute to engineering standards and team-wide tooling.
- *Documentation:*Document methodology, assumptions, and validation in Confluence; produce design docs, ADRs, runbooks, and model cards suitable for regulatory and clinical review. Documentation is a deliverable, not an afterthought.
Required Qualifications
- Bachelor's in Machine Learning, Statistics, Operations Research, Applied Math, Computer Science, or related field.
- 7+ years of hands-on ML/AI and software engineering with production ownership, including delivery of forecasting and/or optimization systems in production.
- Deep expertise in model development and training across both classical ML and deep learning, including time-series and hierarchical or multi-series settings.
- Excellent Python; fluent with NumPy, pandas, scikit-learn, statsmodels, PyTorch, and a forecasting stack (Prophet, GluonTS, Nixtla, or PyTorch Forecasting).
- Track record of moving models from research code into production environments, beyond notebooks and prototypes, including 3+ years building ML services on AWS without relying on SageMaker as the primary ML platform. Comfortable with Docker, Kubernetes-based deployment, and GitHub Actions CI/CD.
- Strong software-engineering habits: testing, packaging, CI, code review, observability.
- Working in Agile/Scrum and documenting in Confluence.
Preferred Qualifications:
- Master's or PhD in Machine Learning, Statistics, Operations Research, Applied Math, Computer Science, or related field.
- Experience in healthcare, pharmacy, supply chain, or other regulated domains, and in environments with formal change-control and validation requirements.
- Background in mathematical optimization (LP/MIP/convex/stochastic) and integration with ML predictions.
- Experience with foundation models for time-series (TimesFM, Chronos, Moirai) and structured adaptation for tabular and temporal tasks.
- Familiarity with causal inference, counterfactual evaluation, and uplift modeling.
- Hands-on experience building AI agents and agentic workflows using orchestration frameworks (LangChain, LangGraph, LlamaIndex, CrewAI), including tool and function calling, guardrails, and error recovery.
- Experience with RAG architectures, vector databases (Pinecone, Weaviate, pgvector), and systematic eval tooling (LangSmith or similar).
- Experience with model serving (vLLM, Triton, TorchServe) and inference optimization (quantization, ONNX, TensorRT).
- Experience with experiment tracking and model registries (MLflow, Weights & Biases).
- Professional use of AI-assisted development tools (Claude Code, Copilot, Cursor) with strong judgment around correctness, security, licensing, and review discipline.
- Publications in top ML/AI conferences (NeurIPS, ICML, KDD, INFORMS, MSOM).
Team Culture
We are building a high-ownership, mission-driven team, energized by the opportunity to solve hard problems that make a difference in patients' lives.
- High ownership - You take full responsibility for outcomes. You identify problems early and drive them to resolution, taking initiative rather than waiting for direction.
- Entrepreneurial spirit - You think and act like an owner: resourceful, creative, willing to challenge assumptions, and consistently focused on impact. You find practical paths forward through difficult problems.
- Mission-driven work ethic - You are motivated by the work we are doing to advance the world of health and improve patient outcomes, and you bring genuine energy and commitment to that mission. You are driven by a real sense of purpose in your work.
- Creative problem-solving - You are comfortable working through ambiguity. You dig in, experiment, iterate, and deliver, and you are energized by the challenge of figuring things out and finding the right solution.
- AI-native mindset - You actively use the latest AI tools to accelerate your own work across coding, research, design, and documentation, and you are eager to keep advancing what is possible with AI in your craft.
Why Join Us?
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
USA CA - Irvine Laguna Canyon Additional Locations
Work Shift
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
$161,400.00 - $258,200.00 USD Annual
Salary Context
This $161K-$258K range is above the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. Disclosed range: $161K to $258K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
BD AI Hiring
BD has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Laguna Canyon, CA, US. Compensation range: $258K - $258K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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