About the Role
Note: The job is a remote job and is open to candidates in USA. Clinician Nexus is a company that enables health care organizations to build thriving clinician teams with innovative technology products. They are seeking a highly skilled Machine Learning Engineer to develop and deploy machine learning models and advanced data analytics solutions, collaborating with cross-functional teams to drive data-informed decision-making.
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 agentsWrite clean, maintainable, production-quality Python code that integrates smoothly with existing engineering and deployment infrastructureWork with large datasets to clean, preprocess, and analyze data, ensuring data quality and integrityImplement and optimize algorithms using best practices in machine learning, deep learning, and statistical analysisCollaborate with business stakeholders to understand requirements and deliver data-driven solutions that provide actionable insightsDevelop and maintain scalable pipelines and infrastructure for data processing and model training, versioning, deployment, and monitoringEvaluate the performance of machine learning models, including LLM-specific evaluation approaches, and tune models for optimal performanceCommunicate findings, insights, and model performance to both technical and non-technical audiencesContinuously stay updated on the latest trends, technologies, and best practices
Skills
Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, or a related field. or related experienceBachelor with 5+ years of relevant experienceMaster or higher with 3+ years of relevant experienceFluent in Python (3+ years of coding experience)Strong software development practices in Python, including writing maintainable, testable, production-ready codeSolid understanding of LLM architectures and Generative AIHands-on experience building and evaluating RAG pipelinesExperience 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 PandasFamiliarity 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), including approaches for evaluating non-deterministic outputsExperience with model management tools such as MLFlow and the model development life cycleExperience with version control tools such as GitProficiency in adapting SDLC best practices for code development and testingExcellent problem-solving skills, analytical thinking, and the ability to work in a fast-paced environmentStrong communication skills and the ability to explain complex technical concepts to non-technical stakeholdersCollaborator: work effectively with others, including domain experts, engineers, and business stakeholdersInquisitive: desire to ask questions and get a deeper understanding of issuesInnovative: ability to imagine new analytical solutions to any problemConfident: able to challenge perceptions and biases of individuals at every level of the organizationCurious: stays abreast of current and upcoming technologies and toolsBusiness-oriented: solid understanding of business requirements and vernacularFamiliarity with optimizing, deploying and scaling automated training pipelines of transformer-based modelsFamiliarity with distributed training techniques and GPU-accelerated computingFamiliarity with classical NLP approachesExperience implementing CI/CD pipelines for ML models for automating training, validation, monitoring, and scalable deploymentExperience with integrating and deploying AWS AI/ML servicesExperience with DatabricksExperience in Health Care data
Benefits
Medical and dental coverage at no premium cost for employees401(k) and profit-sharing retirement plansFlexible spending accountsPaid time off (PTO)Company-paid holidaysGender-neutral parental leaveBereavement and pet leaveContinuing education and professional accreditation sponsorshipLife and AD&D insuranceShort- and long-term disabilityEmployee assistance programMental health support program