We’re hiring a Part-Time Machine Learning Engineer (10–30 hrs/week) to help design, build, and evaluate healthcare-focused ML models. You’ll work closely with our AI and clinical data teams to develop models that improve decision-making, automate key analytics workflows, and drive better patient outcomes. This is a great fit for someone with a strong applied ML background who’s passionate about healthcare innovation and eager to contribute to meaningful projects on a flexible schedule.

Key Responsibilities

Model Development & Evaluation

• Build, train, and optimize machine learning models using healthcare datasets (structured and unstructured).
• Design and run experiments for model validation, benchmarking, and interpretability.
• Support feature engineering and data preprocessing for multimodal healthcare data (e.g., EMR, labs, imaging, wearable data).
• Implement and document reproducible ML pipelines (e.g., using Vertex AI, PyTorch, or scikit-learn).

Clinical ML Research

• Explore and prototype models for tasks such as risk prediction, outcome forecasting, and report summarization.
• Collaborate with clinicians and domain experts to define relevant features and evaluation metrics.
• Ensure model transparency, safety, and performance consistency across diverse patient groups.

Tooling & Automation

• Integrate trained models with downstream analytics or decision-support tools.
• Develop scripts for monitoring, retraining, and performance tracking.
• Maintain clean, well-documented code for reproducibility and auditability.

Collaboration & Learning

• Work closely with cross-functional teams (AI/ML, data science, clinical research).
• Participate in design discussions around ML architecture and model governance.
• Stay up to date on state-of-the-art methods in healthcare ML and contribute to internal knowledge sharing.

Preferred Qualifications

• B.S. or M.S. in Computer Science, Machine Learning, Biomedical Engineering, or a related field.
• Strong experience with Python-based ML frameworks (TensorFlow, PyTorch, scikit-learn).
• Familiarity with healthcare data types and standards (e.g., EMR, FHIR, ICD codes, clinical notes).
• Experience with cloud-based ML platforms (e.g., GCP Vertex AI, AWS Sagemaker).
• Understanding of model evaluation metrics (AUC, F1, calibration, fairness).
• Interest in explainable AI, clinical validation, and ethical ML practices.

Why Join Nimblemind.ai?

• Contribute directly to building ML systems that improve healthcare outcomes.
• Flexible part-time schedule (10–30 hours per week).
• Collaborate with a world-class, mission-driven team of AI engineers and clinicians.
• Competitive hourly compensation and equity in a rapidly growing startup.