Machine Learning Engineer, Data Scientist

🕒 il y a 1 mois

🌵 Arizona, California, +14 états de plus – Distant

🌵 Arizona – Remote 🏄 California – Remote ⛰️ Colorado – Remote 🐊 Florida – Remote 🌽 Illinois – Remote 🏖️ New Jersey – Remote 🗽 New York – Remote 🌲 North Carolina – Remote 🏈 Ohio – Remote 🍂 Massachusetts – Remote 🚗 Michigan – Remote ❄️ Minnesota – Remote 🏰 Missouri – Remote 🔔 Pennsylvania – Remote 🤠 Texas – Remote 🧀 Wisconsin – Remote

💵 €100 700 - €167 800 / an

⏰ Temps Plein

🟡 Intermédiaire

🟠 Senior

🚰 Ingénieur Data

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Clinician Nexus

Site WebLinkedInTous les Emplois

51 - 200 employés

Fondée en 2016

🏥 Santé

⚕️ Assurance santé

📚 Éducation

💰 Seed Round en 2019-12

Healthcare • Healthcare Insurance • Education

Clinician Nexus est une plateforme qui permet aux systèmes de santé de constituer une main-d'œuvre clinique de haute qualité grâce à la transparence et à un aperçu de la productivité, de la gestion des pratiques cliniques et de l'apprentissage clinique. Elle offre des outils pour la gestion des rémunérations et la gestion des étudiants, prenant en charge plus de 50 000 cliniciens et 143 500 étudiants à travers plus de 2 400 sites cliniques. Clinician Nexus est dédié à aider les organisations de santé à éduquer, engager, et fidéliser leur personnel clinique à chaque étape de leur carrière.

Description

• 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

🎯 Exigences

• 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.

🏖️ Avantages

• 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