Designing, developing, and implementing machine learning models and algorithms at PayPal. Collaborating with data scientists and software engineers to enhance services through innovative AI/ML solutions.
About the role
- Machine Learning Engineer developing AI/ML facing applications for Roche's drug discovery process. Collaborating with scientists and engineers to optimize research through AI technologies and methodologies.
Responsibilities
- Work closely with Computational Scientists, Research Scientists, AI/ML experts, Product leaders, DevOps, and others
- Design, develop, and test robust, scalable, and maintainable AI/ML facing scientific web applications and backend systems
- Build tools to evaluate AI/ML model performance and establish new ways to understand AI quality
- Partner with product managers and scientists to understand user needs, shape requirements, and translate them into actionable technical specifications
- Develop and maintain systems for collecting, structuring, and storing diverse scientific data that support advanced analytics, machine learning, and other data-driven initiatives
- Implement, adopt, or evaluate new AI/ML algorithms and analytical techniques
- Contribute to architectural decisions, code reviews, and the evolution of our development processes
- Stay up-to-date with emerging technologies and industry best practices and adopt a culture of continuous learning, collaboration, and curiosity.
Requirements
- Bachelor's or Master’s in Computer Science or similar technical field
- 2+ years of professional experience in machine learning or similar relevant experience
- Strong proficiency with AI/ML frameworks, libraries, and toolsets
- Expert knowledge of statistics, machine learning theory, and algorithms
- Strong knowledge of ML performance optimization, GPU best practices
- Experience with kubernetes, relational databases, NoSQL databases, or data lakes
- Experience working on cloud-native architectures in public clouds (ideally AWS)
- Proven understanding and application of engineering best practices
- Excellent communication skills and ability to build trusted partnerships with internal and external collaborators
- Ability to quickly acquire new technologies and programming languages and a passion for continuous learning.
- Preferred But Not Required: Experience with imaging or biological data and processes is a strong plus
- Experience working with scientists or in a research environment is advantageous
- Experience with workflow automation, GenAI, and/or agents is a plus.
Benefits
- Health insurance
- Retirement plans
- Paid time off
- Flexible work arrangements
- Professional development opportunities
- Discretionary annual bonus
- Relocation benefits
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