Machine Learning Engineer
Skills
- Proficiency in Python and ML frameworks such as PyTorch or TensorFlow.
- Strong understanding of MLOps practices including model deployment, CI/CD pipelines, and performance monitoring.
- Experience with cloud platforms such as AWS, GCP, or Azure and tools like Docker and Terraform.
- Familiarity with healthcare data and clinical workflows is a plus.
- Exceptional problem-solving and ownership mindset.
- Collaborative teamwork skills.
Responsibilities
- Develop, deploy, and maintain state-of-the-art machine learning models for medical imaging, NLP, and multimodal tasks.
- Design and implement robust, scalable ML pipelines and shared infrastructure to support agile experimentation and deployment.
- Collaborate with researchers to translate novel algorithms into production-ready solutions.
- Build and maintain MLOps tools and practices, including automated testing, continuous integration, and monitoring of deployed models.
- Optimize model performance for speed, reliability, and scalability in production environments.
- Partner with clinicians, engineers, and product teams to align machine learning efforts with clinical and product needs.
Education
- Bachelor’s degree in Computer Science, Engineering, or a related field.
Benefits
- Competitive salary and benefits package.
- Flexible working hours with a fully remote position and some travel required.
- Health insurance and retirement plans.
- Professional development support and opportunities.
- Collaborative and innovative work environment.
Average salary estimate
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