AI​/ML Engineer - Precision Health

AI/ML Engineer – Precision Health

Join Sci.bio Recruiting, a Precision Health startup in Boston, MA, to design, train, and deploy machine learning models that power a digital twin platform for real‑time simulations. You will build scalable pipelines for omics and clinical data, focusing on privacy‑preserving and federated learning.

• 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 and create informative visualizations for stakeholders.
• Collaborate with Data Scientists on data cleaning, statistical analysis, feature engineering, and model selection pipelines.
• 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 deployment, monitoring, and retraining.
• Adhere to best coding, testing, and reusable component design practices.
• Adapt flexibly across data engineering and machine learning projects based on 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.

• MSc/PhD in Computer Science, AI/ML, or a related field.
• 3–6 years of ML engineering experience, ideally in biomedical applications within the pharmaceutical or healthcare industry.
• Strong programming skills in Python or R; expertise with libraries for data manipulation, statistical analysis, visualization, and ML frameworks.
• Proficiency with Python, Tensor Flow, PyTorch, scikit‑learn, and familiarity with Keras.
• Experience with PySpark Data Frames and data processing libraries.
• Hands‑on experience across the ML lifecycle: feature stores, MLflow, model registry, deployment/serving, and monitoring.
• Sound analytical and problem‑solving skills; ability to learn quickly and handle 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.

Boston, MA – Fully onsite. Competitive salary ($130k–160k) with benefits.