Strong background in Python and modern ML frameworks (e.g., Huggingface, pyTorch). * Familiarity with healthcare workflows and regulatory requirements (e.g., HIPAA, FHIR standards). * Hands-on ...
Strong background in Python and modern ML frameworks (e.g., Huggingface, pyTorch). * Familiarity with healthcare workflows and regulatory requirements (e.g., HIPAA, FHIR standards). * Hands-on ...
Pytorch Huggingface information
What are the key skills and qualifications needed to thrive as a PyTorch Hugging Face Engineer, and why are they important?
To thrive as a PyTorch Hugging Face Engineer, you need a strong background in deep learning, Python programming, and experience with machine learning frameworks, supported by a relevant degree such as computer science or engineering. Familiarity with PyTorch, Hugging Face Transformers library, version control systems like Git, and often cloud platforms (e.g., AWS, GCP) is essential, with certifications in machine learning or cloud technologies being advantageous. Strong problem-solving skills, collaboration, and clear communication help you effectively design, implement, and optimize NLP models in cross-functional teams. These skills ensure you can build state-of-the-art AI solutions efficiently, troubleshoot complex challenges, and deliver impactful results in the fast-evolving field of natural language processing.
How do PyTorch Huggingface engineers typically collaborate with data scientists and researchers in a project setting?
PyTorch Huggingface engineers often work closely with data scientists and researchers to implement, fine-tune, and deploy state-of-the-art machine learning models. Collaboration involves regular discussions to understand project objectives, translating research ideas into efficient code, and iterating on model performance. Engineers are responsible for optimizing model pipelines, integrating new features, and ensuring compatibility with the Huggingface ecosystem. Effective communication and teamwork are essential, as projects usually require frequent feedback loops and joint problem-solving sessions.
What are Pytorch Huggingface developers?
PyTorch Hugging Face developers are professionals who specialize in building and deploying machine learning and natural language processing (NLP) models using PyTorch, an open-source deep learning framework, and the Hugging Face library, which provides a wide range of pre-trained models and tools for NLP tasks. These developers create, fine-tune, and implement models for tasks like text classification, question answering, and language generation. Their expertise includes working with model architectures such as BERT, GPT, and others, as well as integrating models into applications or research projects.
What is the difference between Pytorch Huggingface vs Machine Learning Engineer?
| Aspect | Pytorch Huggingface | Machine Learning Engineer |
|---|---|---|
| Credentials | Proficiency in Python, deep learning frameworks, familiarity with NLP libraries | Degree in CS, data science, or related field; experience with ML models |
| Work Environment | Research labs, AI startups, tech companies focusing on NLP and deep learning | Tech companies, consulting firms, R&D departments across industries |
| Usage | Developing NLP models, fine-tuning transformers, deploying AI solutions | Designing, building, and deploying ML models across various domains |
While Pytorch Huggingface specializes in NLP model development using transformer architectures, Machine Learning Engineers work across diverse ML applications. Pytorch Huggingface skills are often part of a Machine Learning Engineer's toolkit, but the roles differ in scope and focus.
What are popular job titles related to Pytorch Huggingface jobs in Raleigh, NC? For Pytorch Huggingface jobs in Raleigh, NC, the most frequently searched job titles are:
What job categories do people searching Pytorch Huggingface jobs in Raleigh, NC look for? The top searched job categories for Pytorch Huggingface jobs in Raleigh, NC are:
Full-time
Posted 21 days ago
Job description
About Keebler Health
Keebler Health is building the operating system for value-based care. Our mission is to help risk-bearing healthcare organizations thrive in value-based arrangements by unlocking the full power of their data. We empower leading primary care groups, ACOs, and health plans to act on real-time insights that improve outcomes, reduce costs, and fuel sustainable growth.
We're a fast-moving, high-performing team, and we're looking for people who share our bias toward speed, urgency, and excellence. As a member of the team, you won't just write code or stay in your lane-you'll shape critical systems, innovate quickly, and set a high bar for a product that supports the future of U.S. healthcare.
About the role
We are seeking a talented and motivated mid to senior level AI Engineer with expertise in developing and fine-tuning large language models (LLMs), healthcare workflows, and AI/ML engineering best practices. The ideal candidate will bring a deep understanding of healthcare-specific challenges and modern AI techniques to drive innovation in Value-Based Care solutions. Level and salary will commensurate with experience.
Key Responsibilities
AI/ML Engineering
Collaboration and Impact
Required Qualifications
Preferred Skills
When applying
In addition to your resume, also include:
What We Offer
Keebler Health is building the operating system for value-based care. Our mission is to help risk-bearing healthcare organizations thrive in value-based arrangements by unlocking the full power of their data. We empower leading primary care groups, ACOs, and health plans to act on real-time insights that improve outcomes, reduce costs, and fuel sustainable growth.
We're a fast-moving, high-performing team, and we're looking for people who share our bias toward speed, urgency, and excellence. As a member of the team, you won't just write code or stay in your lane-you'll shape critical systems, innovate quickly, and set a high bar for a product that supports the future of U.S. healthcare.
About the role
We are seeking a talented and motivated mid to senior level AI Engineer with expertise in developing and fine-tuning large language models (LLMs), healthcare workflows, and AI/ML engineering best practices. The ideal candidate will bring a deep understanding of healthcare-specific challenges and modern AI techniques to drive innovation in Value-Based Care solutions. Level and salary will commensurate with experience.
Key Responsibilities
AI/ML Engineering
- Fine-tune and optimize large language models (LLMs) to address specific healthcare applications.
- Develop and apply advanced prompt engineering techniques to enhance model outputs for clinical scenarios.
- Implement Retrieval-Augmented Generation (RAG) systems to improve knowledge retrieval from large datasets.
- Work with knowledge graphs to organize and integrate healthcare-specific data for enhanced decision-making.
- Evaluate black-box models using precision, recall, and other performance metrics, ensuring robustness and reliability.
- Collaborate with healthcare professionals to understand workflows and identify opportunities for AI-driven enhancements.
- Design and build AI models that align with healthcare standards and regulations (e.g., HIPAA compliance).
- Integrate domain-specific knowledge of healthcare data, including FHIR and interoperability standards, into AI solutions.
- Develop and maintain scalable, production-ready AI pipelines using MLOps tools.
- Deploy and monitor AI models in production environments to ensure performance and compliance.
- Optimize infrastructure for efficient training, testing, and deployment of models.
- Stay at the forefront of advancements in AI, especially in healthcare applications.
- Identify and resolve performance bottlenecks in AI workflows.
- Explore emerging trends and technologies in LLMs and healthcare to continually improve solutions.
Collaboration and Impact
- Partner with cross-functional teams, including data engineers and clinicians, to ensure seamless integration of AI into healthcare workflows.
- Communicate technical results and insights effectively to non-technical stakeholders.
Required Qualifications
- Proven experience in LLM fine-tuning and advanced prompt engineering.
- Strong background in Python and modern ML frameworks (e.g., Huggingface, pyTorch).
- Familiarity with healthcare workflows and regulatory requirements (e.g., HIPAA, FHIR standards).
- Hands-on experience with retrieval-augmented generation (RAG) techniques.
- Expertise in evaluating AI models using performance metrics like precision, and recall.
Preferred Skills
- Experience with MLOps frameworks such as MLflow, Langfuse, or similar tools.
- Understanding of healthcare data standards, including HL7 and HEDIS metrics.
- Strong problem-solving skills in integrating AI with complex healthcare datasets.
- Familiarity with cloud platforms (e.g., AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
When applying
In addition to your resume, also include:
- A highly personalized, bold, and hilarious "Keebler Health-style" introduction that grabs attention - outgoing, fun, and uniquely you (not uniquely ChatGPT). Think: confident, high-energy, slightly irreverent (but still professional), with a smart nod to healthcare, value-based care, and the fact that we're building something real.
What We Offer
- Competitive salary and benefits package.
- Opportunity to work in a fast-paced, innovative environment.
- Professional growth and development opportunities.
- Collaborative and supportive team culture.
- Chance to make a meaningful impact on the healthcare industry.