About the Company A fast-growing health-tech company building AI-powered solutions to improve healthcare workflows, decision-making, and patient experiences. The team works at the intersection of machine learning, healthcare data, and modern software engineering , with a strong focus on deploying practical AI systems into production.
About the Role
We’re looking for a Machine Learning Engineer to design, develop, and deploy ML models that solve real-world healthcare problems. You’ll work closely with product and engineering teams to take models from experimentation to reliable production systems.
What You’ll Do
- Develop and productionize machine learning models for healthcare use cases.
- Work with structured and unstructured datasets to identify meaningful patterns.
- Build data preprocessing, feature engineering, training, and evaluation pipelines.
- Experiment with modern ML and deep learning techniques.
- Work with NLP/LLM-based solutions where applicable.
- Deploy and monitor ML models in production environments.
- Optimize models for accuracy, scalability, latency, and reliability.
- Collaborate with software engineers and product teams to integrate ML capabilities into applications.
Requirements
- 2–5 years of experience in Machine Learning/AI engineering.
- Solid Python programming skills.
- Experience with PyTorch, TensorFlow, or Scikit-learn .
- Strong understanding of ML algorithms, statistics, and model evaluation.
- Experience working with data processing and ML pipelines.
- Knowledge of SQL and experience working with large datasets.
- Familiarity with REST APIs, Docker, and cloud platforms.
- Experience taking ML models from experimentation to production.
Good to Have
- Experience with NLP, LLMs, Generative AI, or healthcare AI .
- Experience with AWS/GCP/Azure.
- Knowledge of MLOps and model monitoring.
- Experience working with healthcare or clinical datasets.
Why Join?
- Work on AI problems with real-world healthcare impact .
- Fully remote opportunity.
- Opportunity to work closely with experienced AI and product teams.
- High ownership and exposure to production-grade ML systems.