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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in developing and maintaining Python components for machine learning applications, with a strong focus on integrating and fine-tuning Transformer-based models. Proficient in deploying models and optimizing performance across various infrastructures.
Highest-signal resume keywords
Python ProgrammingMachine Learning FrameworksTransformer ArchitecturesModel DeploymentCI/CD
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
PythonMachine LearningModel InferencePerformance OptimizationTestingVersion ControlData PipelinesDistributed ComputingExperiment TrackingSoftware Engineering Fundamentals
Soft Skills
Problem-Solving MindsetClear CommunicationCollaborative Mindset
Tools & Technologies
PyTorchHugging FaceDockerAPIsLinuxVLLMRayKubernetesCloud GPU Infrastructure
Certifications & Qualifications
Master’s Degree in Computer ScienceMaster’s Degree in Artificial IntelligenceMaster’s Degree in Machine LearningMaster’s Degree in Software Engineering
Industry Keywords
Machine Learning ApplicationsData-Processing PipelinesModel PerformanceTechnical ProblemsOpen-Source Contributions
Tech Stack
Tools & technologiesCloudDockerKubernetesLinuxPythonPyTorchRay
About the role
Key responsibilities & impact- Develop and maintain Python components for machine learning applications
- Integrate, fine-tune and evaluate Transformer-based models
- Help build data-processing, training and inference pipelines
- Support deployment of models into reliable applications and services
- Measure and improve model performance, latency and resource usage
- Write tests and contribute to code quality, documentation and reproducibility
- Investigate technical problems across models, software and infrastructure
- Work closely with researchers to turn experimental code into maintainable systems
- Learn how AI workloads are deployed across cloud, private and on-premises infrastructure
Requirements
What you’ll need- A completed or nearly completed Master’s degree in computer science, artificial intelligence, machine learning, software engineering or a related technical field
- Strong Python programming skills
- Practical experience with PyTorch or another modern machine learning framework
- Familiarity with large language models and Transformer architectures
- Understanding of software-engineering fundamentals, including testing, version control and maintainable code
- Experience gained through a thesis, internship, university project, open-source contribution or personal project
- A practical problem-solving mindset and willingness to work across different parts of the technology stack
- Clear communication skills and a collaborative mindset
- Experience with Hugging Face, Docker, APIs or Linux
- Familiarity with vLLM, Ray, Kubernetes or cloud GPU infrastructure
- Experience deploying a machine learning model or application
- Understanding of model inference, distributed computing or performance optimisation
- Experience with CI/CD, experiment tracking or data pipelines
- Open-source contributions or relevant technical projects
Benefits
Comp & perks- Hybrid working model
- Regular collaboration at the Lausanne office
- Practical experience with model inference, data pipelines, APIs, GPU infrastructure and production deployment
- Opportunity to work with research and engineering teams
- Opportunity to gain practical experience building, testing and deploying AI systems
- Learning exposure to AI workloads across cloud, private and on-premises infrastructure