Healthcare ML Engineer

Healthcare ML Engineer

Position Overview

Join our innovative team as a Healthcare Machine Learning Engineer, where you’ll design, build, and deploy cutting-edge ML models that transform healthcare delivery. This role combines expertise in machine learning, software engineering, and healthcare domain knowledge to create AI solutions that improve diagnostics, predict patient outcomes, optimize operations, and enhance clinical decision-making. You’ll work directly with data scientists, clinicians, and product teams to bring healthcare AI from prototype to production at scale.

Location: Remote/Hybrid (San Francisco, Boston, Chicago, or Austin preferred)
Salary Range: $130,000 – $180,000 + Equity + Performance Bonus
Employment Type: Full-Time

Key Responsibilities

Model Development & Training

• Design and implement machine learning models for healthcare applications including:

• • Predictive analytics for patient risk stratification and early disease detection
  • Natural language processing for clinical documentation and medical coding
  • Computer vision for medical imaging analysis (radiology, pathology, dermatology)
  • Time-series forecasting for capacity planning and resource optimization

• Develop custom ML architectures optimized for healthcare data characteristics (sparse, multimodal, longitudinal)
• Conduct feature engineering using electronic health records, medical imaging, genomics, and wearable device data
• Implement transfer learning and fine-tuning strategies to leverage pre-trained models for healthcare-specific tasks

ML Infrastructure & MLOps

• Build scalable ML pipelines for data preprocessing, model training, validation, and deployment
• Implement continuous integration/continuous deployment (CI/CD) workflows for ML models
• Develop model monitoring systems to track performance drift, data quality issues, and bias detection
• Create automated retraining pipelines that adapt models to evolving healthcare data distributions
• Optimize model inference for real-time clinical decision support (sub-second latency requirements)

Collaboration & Integration

• Partner with data scientists to translate research prototypes into production-ready systems
• Work with software engineers to integrate ML models into healthcare applications and EHR systems
• Collaborate with clinical stakeholders to validate model outputs and ensure clinical relevance
• Engage with product managers to define ML capabilities that address real healthcare needs

Research & Innovation

• Stay current with latest ML research applicable to healthcare (papers from NEJM AI, Nature Medicine, NeurIPS Health)
• Experiment with emerging techniques: federated learning, few-shot learning, explainable AI (XAI)
• Contribute to open-source healthcare ML projects and internal knowledge sharing
• Prototype novel approaches to challenging healthcare ML problems

Compliance & Safety

• Ensure ML models meet healthcare regulatory requirements (FDA guidelines for medical devices, HIPAA compliance)
• Implement fairness and bias mitigation strategies to prevent health disparities
• Document model development, validation, and limitations for clinical safety reviews
• Participate in clinical validation studies and regulatory submission processes

Required Qualifications

Education

• Master’s degree in Computer Science, Data Science, Machine Learning, Biomedical Engineering, or related field
• PhD preferred for research-intensive roles
• Relevant bootcamp graduates with 3+ years professional ML experience considered

Experience

• 3+ years of hands-on experience building and deploying production ML systems
• 1+ years working with healthcare data (EHRs, medical imaging, claims data, genomics)
• Proven track record shipping ML models that solved real business problems at scale
• Experience with full ML lifecycle: data collection, labeling, training, evaluation, deployment, monitoring

Technical Skills

Programming & ML Frameworks

• Expert proficiency in Python with strong software engineering practices
• Deep experience with ML frameworks: TensorFlow, PyTorch, scikit-learn
• Familiarity with specialized libraries: Hugging Face (NLP), OpenCV (computer vision), Pandas/NumPy (data manipulation)

MLOps & Infrastructure

• Experience with cloud platforms: AWS (SageMaker, EKS), Google Cloud (Vertex AI), or Azure (Machine Learning)
• Proficiency with containerization: Docker, Kubernetes
• Knowledge of ML orchestration tools: MLflow, Kubeflow, Airflow
• Experience with model serving frameworks: TensorFlow Serving, TorchServe, ONNX Runtime

Data Engineering

• SQL and NoSQL database experience (PostgreSQL, MongoDB)
• Big data processing: Apache Spark, Hadoop (bonus)
• Data pipeline development using Kafka, Airflow, or similar
• Experience with data versioning and experiment tracking (DVC, Weights & Biases)

Healthcare-Specific

• Understanding of healthcare data formats: HL7, FHIR, DICOM
• Experience with medical terminology, ICD-10, CPT codes
• Familiarity with healthcare privacy regulations and de-identification techniques
• Knowledge of clinical workflows and decision-making processes

Preferred Qualifications

• Publications in top-tier ML or medical AI conferences/journals
• Experience with FDA submission processes for AI/ML-based medical devices
• Background in biostatistics or epidemiology
• Contributions to open-source healthcare ML projects (MONAI, FHIR-PYrate, etc.)
• Experience with federated learning or privacy-preserving ML techniques
• Clinical degree (MD, DO, RN) or clinical informatics certification

Core Competencies

• Analytical Rigor: Systematic approach to problem-solving with attention to edge cases
• Pragmatism: Balance perfectionism with shipping production systems on schedule
• Collaboration: Effective communication with non-technical stakeholders
• Curiosity: Passion for learning emerging ML techniques and healthcare challenges
• Quality Focus: Commitment to building safe, reliable ML systems for patient care