Sr. Machine Learning Researcher - Healthcare AI
A
AKASAHealthcare AI
United StatesFull-TimeSenior
Salary175000 - 230000 USD per year
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Job Details
- Experience
- 2-3 years
- Required Skills
- AWSPythonGCPKubeflowKubernetesPyTorchTensorflowLLM
Requirements
- A passion for tackling meaningful healthcare challenges and leveraging AI to make a positive impact on the healthcare system
- Proven experience training and fine-tuning large language models, with expertise in model architecture, optimization techniques, and performance evaluation
- Experience with modeling in the healthcare domain, clinical natural language understanding, and healthcare data and data standards (e.g., EDI, FHIR) is strongly preferred
- Ph.D. or equivalent industry experience in fields related to machine learning, natural language processing, computer vision, computer science, electrical engineering, statistics, mathematics, optimization, or data science, plus 2-3 years of experience
- Peer-reviewed publications in top AI conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ACL, NAACL, and EMNLP) strongly preferred
- Strong programming skills in Python
- Proficiency in deep learning frameworks and tools such as PyTorch, TensorFlow, PyTorch Lightning, Hugging Face Transformers, and Kubernetes/Kubeflow
- Experience with cloud platforms (AWS, GCP) and multi-GPU environments
- A track record of translating research into real-world applications, with experience in rapid prototyping, experimentation, and writing production-ready code
- Ability to work effectively in a cross-functional, fast-paced environment, with strong written and verbal communication skills
Responsibilities
- Drive Applied Research: Lead the design, training, and evaluation of large language models to solve healthcare-specific challenges, advancing the state of the art in clinical Natural Language Understanding
- Leverage Human-in-the-Loop Feedback: Work closely with cross-functional teams to integrate Human-in-the-Loop data, using it to guide model improvements and explore new methods for optimizing performance
- Collaborate Across Teams: Partner with healthcare experts and other stakeholders to integrate qualitative insights, ensuring models align with real-world needs and deliver meaningful results
- Stay on the Cutting Edge: Regularly evaluate advancements in ML to determine their relevance to our work, maintaining AKASA’s leading edge in responsible, high-impact healthcare AI
- Contribute to Broader Impact: Publish and share research findings in the broader AI community, helping to advance healthcare applications of AI through peer-reviewed publications
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