Machine Learning Engineer
Job Description
For our client, we are seeking a Machine Learning Engineer to join the team of a leader in the Healthcare space. This role will lead work at the intersection of data, AI-enabled capabilities, and scalable technology delivery. You will work across engineering, product, operations, and business stakeholders to translate complex requirements into practical technology solutions. The position offers the opportunity to influence architecture, execution quality, and the technology capabilities that enable long-term growth within a healthcare environment.
Location: Remote – US based candidates only, no visa sponsorship available
Compensation: $150,000 – $180,000 annually
Responsibilities
Develop and improve NLP systems and AI experiences
Fine-tune language models for specific use cases
Evaluate model performance and drive quality improvements
Design evaluation frameworks for quality and reliability
Build automated testing pipelines for model assessment
Create monitoring systems for continuous performance evaluation
Partner with teams to ensure responsible AI deployment
Qualifications
3+ years experience in machine learning systems
Strong Python programming skills
Experience with NLP and Large Language Models
Familiar with PyTorch or TensorFlow
Solid understanding of deep learning and modern NLP architectures
Experience in evaluating machine learning models
Familiarity with SQL and large-scale datasets
Benefits
Remote work with subsidized travel for in-person bonding
Holistic perks including free therapy and wellness programs
Excellent health, dental, and vision coverage
401k benefits with employer matching
Provision of necessary hardware and software for productivity
An opportunity to make a significant impact on user experiences
Supportive and collaborative workplace culture
Our client is an equal opportunity employer. We encourage you to apply even if you don’t meet every qualification—your background could be exactly what this team needs.
Desired Skills and Experience
Python, PyTorch, TensorFlow, Natural Language Processing (NLP), Large Language Models (LLMs), SQL, Open-source language models, Machine learning frameworks, Deep learning architectures, Model evaluation frameworks, Cloud platforms (AWS, GCP, Azure), GPU infrastructure, CI/CD tools, Monitoring and analytics tools, Data processing frameworks (e.g., Apache Spark)