Machine Learning Engineer, Data Scientist
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Job Description
Machine Learning Engineer at Clinician Nexus developing advanced ML models and data analytics solutions. Collaborate with cross-functional teams to solve real-world healthcare problems.
Responsibilities:
- Design, develop, and deploy ML solutions ranging from traditional ML applications (classification, clustering, recommendations) to LLM-based systems, including document parsing, data extraction, RAG pipelines, and LLM agents
- Write clean, maintainable, production-quality Python code that integrates smoothly with existing engineering and deployment infrastructure
- Work with large datasets to clean, preprocess, and analyze data, ensuring data quality and integrity
- Implement and optimize algorithms using best practices in machine learning, deep learning, and statistical analysis
- Collaborate with business stakeholders to understand requirements and deliver data-driven solutions that provide actionable insights
- Develop and maintain scalable pipelines and infrastructure for data processing and model training, versioning, deployment, and monitoring
- Evaluate the performance of machine learning models, including LLM-specific evaluation approaches, and tune models for optimal performance
- Communicate findings, insights, and model performance to both technical and non-technical audiences
- Continuously stay updated on the latest trends, technologies, and best practices
Requirements:
- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, or a related field
- Bachelor with 5+ years of relevant experience
- Master or higher with 3+ years of relevant experience
- Fluent in Python (3+ years of coding experience)
- Strong software development practices in Python, including writing maintainable, testable, production-ready code
- Solid understanding of LLM architectures and Generative AI
- Hands-on experience building and evaluating RAG pipelines
- Experience with LLM orchestration frameworks (LangChain, LlamaIndex, or similar)
- Proficiency in machine learning libraries such as Scikit-learn and PyTorch; and fundamental libraries such as NumPy and Pandas
- Familiarity with cloud platforms (e.g., AWS, GCP, Azure) and containerization tools (e.g., Docker)
- Strong understanding of model evaluation metrics across traditional ML (e.g., accuracy, precision, recall, F1) and LLM-based systems (e.g., faithfulness, answer relevancy, hallucination detection)
- Experience with model management tools such as MLFlow and the model development life cycle
- Experience with version control tools such as Git
- Proficiency in adapting SDLC best practices for code development and testing
- Excellent problem-solving skills, analytical thinking, and the ability to work in a fast-paced environment
- Strong communication skills and the ability to explain complex technical concepts to non-technical stakeholders.
Benefits:
- Competitive total compensation package
- Medical and dental coverage at no premium cost for employees
- 401(k) and profit-sharing retirement plans
- Flexible spending accounts
- Paid time off (PTO)
- Company-paid holidays
- Gender-neutral parental leave
- Bereavement and pet leave
- Continuing education and professional accreditation sponsorship
- Life and AD&D insurance
- Short- and long-term disability
- Employee assistance program
- Mental health support program
- Additional perks
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