AI/ML Engineer - Precision Health

Seeking an AI/ML Engineer to join a Precision Health startup in Boston, MA to design, train, and deploy machine learning models powering a digital twin platform for real-time simulations. You’ll build scalable pipelines for omics and clinical data, with a focus on privacy-preserving and federated learning.

Responsibilities

• Build, train, and evaluate ML models for multi-omics and clinical data integration.
• Develop scalable data and model pipelines, including data cleaning, transformation, and orchestration.
• Explore and understand diverse biotech/pharma/healthcare datasets; create informative, appealing data visualizations for stakeholders.
• Work with Data Scientists to perform data cleaning, statistical analysis, and feature engineering; develop pipelines for model selection, training, and evaluation.
• Ensure consistent feature engineering between training and model serving to prevent training-serving skew.
• Implement privacy-preserving ML techniques, including federated learning approaches.
• Partner with data engineering and software teams to deploy models to clinical dashboards; automate model deployment, monitoring, and retraining.
• Adhere to best practices for coding, testing, and designing reusable components.
• Remain flexible across data engineering and machine learning projects based on team backlog and product priorities.
• Evaluate and adopt tools/technologies that improve ETL performance and MLOps efficiency.
• Collaborate effectively in cross-functional teams with data engineers, analysts, and business stakeholders.
  
  

Required Skills

• MSc/PhD in Computer Science, AI/ML, or related field.
• 3–6 years of ML engineering experience, ideally in biomedical applications. Experience with Pharmaceutical and Healthcare industry.
• Strong programming skills in Python or R; expertise with libraries for data manipulation, statistical analysis, visualization (charts/plots), and ML algorithms/frameworks.
• Proficiency with Python, TensorFlow, PyTorch, scikit-learn; familiarity with Keras.
• Familiarity with PySpark DataFrames and data processing libraries.
• Experience across the ML lifecycle: feature stores, MLflow, model registry, deployment/serving, and monitoring.
• Outstanding analytical and problem-solving skills; ability to learn quickly; hands-on experience with model selection, training, and evaluation.
• Proficiency in statistical techniques and hypothesis testing; experience with regression, clustering, and classification.
• Fully onsite in Boston, MA.
• Salary: $130k–160k Benefits.