eClinical Solutions helps life sciences organizations worldwide accelerate clinical development initiatives with expert data services and the elluminate Clinical Data Cloud – the foundation of digital trials. Together, the elluminate platform and digital data services give clients self-service access to all their data from one centralized location plus advanced analytics that help them make smarter, faster business decisions.
OVERVIEW
The Senior Machine Learning Engineer will work as part of a world-class team developing and deploying AI/ML solutions to transform clinical research. In this role, you will be responsible for designing, developing, and deploying machine learning models at scale, contributing to the entire ML lifecycle from data preprocessing to model optimization and deployment.
KEY TASKS & RESPONSIBILITIES
- Utilize AWS services such as SageMaker, Lambda, EC2, S3, Redshift, and others to build, deploy, manage, and monitor scalable machine learning solutions.
- Develop and maintain automated ML pipelines for continuous integration and deployment (CI/CD) of machine learning models.
- Specify and manage infrastructure using tools like Terraform, Ansible, AWS CloudFormation, and AWS CDK (Cloud Development Kit). Ensure that infrastructure is defined, managed, and deployed as code.
- Optimize machine learning models for performance, scalability, and cost-efficiency. Implement strategies for model tuning, hyperparameter optimization, and resource management on AWS.
- Ensure machine learning systems adhere to security best practices and comply with data protection and privacy regulations.
- Work closely with data engineers, data scientists, software developers, and product managers to integrate ML models into production systems and applications.
- Maintain clear and comprehensive documentation of MLOps processes, workflows, and standard operating procedures.
CANDIDATE’S PROFILE
Education & Experience
- 5+ years of experience as a machine learning engineer. Master’s or PhD degree in STEM preferred.
- Proficiency in Python, R, or other programming languages commonly used in ML.
- Experience deploying and maintaining AI/ML solutions at scale.
- Experience with AWS services such as SageMaker, Lambda, EC2, S3, and Redshift preferred.
- Familiarity with common ML and deep learning frameworks such as PyTorch and Scikit learn.
- Experience running algorithms on various hardware such as CPU vs. GPU.
- Experience with deployment tools such as Docker and scaling frameworks such as Kubernetes.
- Experience specifying infrastructure and working with Infrastructure as Code (IaC) tools like Terraform, Ansible, AWS CloudFormation, and AWS CDK.
- Experience with MLOps practices, including CI/CD for ML models.
- Knowledge of security, privacy, and compliance best practices in ML infrastructure.
- Knowledge of clinical trial or healthcare data is a plus.
Professional Skills
- Team player and collaborator
- Strong communication skills and can explain sophisticated concepts to people outside the field
- Attitude for purpose-driven learning to solve business problems driving business value
- Demonstrate independence and project ownership
Accelerate your skills and career within a fast-growing company while impacting the future of healthcare. We have shared our story, now we look forward to learning yours!
eClinical is a winner of the 2023 Top Workplaces USA national award! We have also received numerous Culture Excellence Awards celebrating our exceptional company vision, values, and employee experience. See all the details here: https://topworkplaces.com/company/eclinical-solutions/
eClinical Solutions is a people first organization. Our inclusive culture values the contribution that diversity brings to our business. We celebrate individual experiences that connect us and that inspire innovation in our community. Our team seeks out opportunities to learn, grow and continuously improve. Bring your authentic self, you are welcome here!
We are proud to be an equal opportunity employer that values diversity. Our management team is committed to the principle that employment decisions are based on qualifications, merit, culture fit and business need.