Machine Learning Engineer, AI Enablement

Try&Hire; Basel; Hybrid; CHF 120,000 - 180,000; Full time

Job summary

Try&Hire is seeking a highly motivated ML infrastructure engineer in Basel to build scalable AI/ML infrastructure, data pipelines and agentic applications for pharmaceutical R&D.

You will translate scientific and business needs into technical designs, write production code, and implement robust testing, CI/CD and documentation. The role is hybrid, with onsite work about 3 days per week.

Qualifications

• Master's degree with 2+ years or PhD in CS or related field.

Responsibilities

• Translate scientific and business needs into technical design and implementation of agentic AI solutions.
• Write, test and ship maintainable production code and build scalable systems and data pipelines.
• Apply software engineering best practices such as testing, CI/CD and documentation.
• Evaluate and scale ML systems using quantitative metrics and AI tooling.
• Contribute to architectural decisions, code reviews and evolution of development processes.
• Work across the stack beyond core area of expertise when needed.

Skills

GPU optimization

Model performance

Backend systems

Cloud-native architecture

Statistics & ML theory

Education

Master's degree in computer science or related field

PhD in computer science or related field

Tools

Kubernetes

AWS

CI/CD tooling

Documentation tools

Job description

This role builds scalable AI/ML infrastructure, data pipelines and agentic applications that enable scientists in pharmaceutical research and development.

Your tasks

• Translate scientific and business needs into technical design and implementation of agentic AI solutions
• Write, test and ship maintainable production code and build scalable systems and data pipelines
• Apply software engineering best practices such as testing, CI/CD and documentation
• Evaluate and scale ML systems using quantitative metrics and AI tooling
• Contribute to architectural decisions, code reviews and evolution of development processes
• Work across the stack beyond core area of expertise when needed

What you bring

• Master's degree with 2+ years of professional experience or a PhD in Computer Science or similar technical field
• Experience across the ML lifecycle: GPU optimization, model performance, backend systems, cloud-native architecture
• Solid knowledge of statistics, ML theory and AI/ML frameworks, libraries and toolsets
• Experience with software engineering best practices: testing, clean code, documentation
• Strong communication skills across technical and non-technical audiences
• Willingness to work onsite approximately 3 days a week

Conditions

• Workload: 100 %
• Work model: Hybrid
• Option of a permanent position after the assignment if both sides want it

What to expect

• Onsite work approximately 3 days a week
• 3–6 months interim, then a permanent position

Technologies

• Kubernetes
• AWS