Staff Machine Learning Engineer

BD; California; Presencial; USD 130.000 - 207.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Medical, dental, vision benefits

401(k) and retirement plan

Paid time off

Descripción de la vacante

BD is hiring a Staff Machine Learning Engineer to develop clinically meaningful AI/ML solutions for continuous physiological monitoring, disease detection, and predictive clinical decision support. You will build end-to-end algorithms and deploy them across cloud systems and edge medical devices, working with multi-modal healthcare data.

You will lead development from data ingestion to model validation, collaborating with clinical, regulatory, and product teams to ensure safety, compliance, and

Formación

• 7+ 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 field.
• Deep expertise in machine learning and AI model development, including classical and modern approaches.
• Hands-on experience with physiological time-series data, biomedical signals, medical device data, or clinical datasets.
• Understanding of human physiology as it relates to patient monitoring.
• Demonstrated experience developing algorithms from early feasibility through productization or deployment.
• Experience with multi-modal healthcare data, including 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 building and deploying algorithms in cloud environments using Azure, AWS, or GCP.
• Experience optimizing models for 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.

Responsabilidades

• Lead the design, development, validation, and deployment of AI/ML algorithms for continuous physiological monitoring, novel vital sign development, disease detection, risk stratification, and early warning systems.
• Develop models using multi-modal sources, including physiological waveforms and time-series signals, bedside and wearable sensor data, EHR data, and structured clinical data.
• Translate clinical and physiological understanding into robust algorithm design, including feature selection, model architecture, performance targets, and clinically interpretable outputs.
• Build end-to-end 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 align development with 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.

Conocimientos

Machine learning

Python

PyTorch

TensorFlow

scikit-learn

Biomedical signal processing

Edge devices

Communication skills

Educación

Master’s degree in Electrical Engineering, Biomedical Engineering, Computer Science, Data Science, Applied Mathematics, Statistics, Computational Biology

Herramientas

Azure

AWS

GCP

Linux

Descripción del empleo

BD is hiring a Staff Machine Learning Engineer to help develop clinically meaningful AI/ML solutions for continuous physiological monitoring, disease detection, and predictive clinical decision support. The role focuses on building end-to-end algorithms and deploying them across cloud systems and edge medical devices, working with multi-modal healthcare data to support monitoring and risk workflows.

What you'll do

• Lead the design, development, validation, and deployment of AI/ML algorithms for continuous physiological monitoring, novel vital sign and physiological parameter development, disease detection, risk stratification, and early warning systems.
• Develop models using multi-modal sources, including physiological waveforms and time-series signals such as PPG, blood pressure, capnography, and other monitoring signals, along with bedside and wearable sensor data, electronic health records, and structured clinical data.
• Translate clinical and physiological understanding into robust algorithm design, including feature selection, model architecture, performance targets, and clinically interpretable outputs.
• Build end-to-end 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 align development with 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.

What you bring

• 7+ 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 classical and modern approaches.
• Hands-on experience with physiological time-series data, biomedical signals, medical device data, or clinical datasets.
• Understanding of human physiology as it relates to patient monitoring.
• Demonstrated experience developing algorithms from early feasibility through productization or deployment.
• Experience with multi-modal healthcare data, including 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 building and deploying algorithms in cloud environments using Azure, AWS, or GCP.
• Experience optimizing models for 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.

Technologies

• Python; PyTorch; TensorFlow; scikit-learn
• Azure; AWS; GCP
• Transformer-based models and foundation-model approaches

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 signal quality assessment, artifact detection, missing-data handling, sensor fusion, uncertainty quantification, and explainable AI.
• Familiarity with MLOps and model lifecycle management, including version control, data governance, automated testing, and post-deployment monitoring.

Compensation and location

• Location: USA CA - Irvine Laguna Canyon (onsite)
• Salary range: USD 129,600 - 207,400 per yearly

Benefits

• Medical, dental, vision, health savings and flexible spending accounts
• Life and disability insurance
• Wellness resources
• Mental health support
• Disease management programs
• 401(k) and deferred compensation programs
• Financial counseling and DailyPay
• Educational savings resources, educational assistance
• Paid time off and company holidays
• Parental leave, adoption assistance, backup care, caregiving support
• Employee assistance programs
• Commuter benefits
• Pet insurance
• Legal services and associate discount programs
• Bereavement leave, military leave, personal leave, family and medical leave, jury duty leave