Machine Learning Scientist (AI for Code)

TL;DR

Machine Learning Scientist (AI for Code): Develop advanced AI and LLM models for source code analysis to improve code quality and security with an accent on applying cutting-edge ML and LLM techniques to software development lifecycle. Focus on designing novel ML models, building data pipelines, and integrating prototypes into products.

Location: On-site in Geneva, Switzerland

Company

hirify.global builds solutions to help organizations produce responsible, secure, and high-quality code quickly, with a global presence and a strong open source community focus.

What you will do

• Spearhead research and innovation in ML, Deep Learning, and LLMs applied to software development lifecycle.
• Develop and validate novel ML models to detect bugs, vulnerabilities, and code smells beyond traditional static analysis.
• Build LLM-powered features including Retrieval-Augmented Generation and fine-tuning on proprietary codebases.
• Engineer robust data pipelines for large-scale code-centric datasets.
• Collaborate with engineering and product teams to integrate ML prototypes into products.
• Communicate complex technical concepts clearly to technical and non-technical stakeholders.

Requirements

• On-site work in Geneva, Switzerland.
• Advanced academic background (Master’s or PhD) in Computer Science, Machine Learning, or related field.
• Strong industry experience in machine learning and modern software engineering practices.
• Proficient in Python and ML/DL frameworks such as PyTorch, TensorFlow, Hugging Face; Java is a plus.
• Experience with NLP or Programming Language Processing and modern LLM architectures and techniques.
• Experience with large-scale data processing and cloud infrastructure (e.g., AWS).
• Excellent English communication skills.

Nice to have

• Experience with fine-tuning strategies like LoRA, QLoRA, and building RAG pipelines.
• Familiarity with Java programming.

Culture & Benefits

• Dynamic, respectful, and kind work culture embracing learning and failure.
• Flexible hybrid work policy with minimum three days in office (Monday/Tuesday/Thursday).
• Global and diverse workforce with 33 nationalities represented.
• Focus on work-life balance and continuous education.
• Commitment to diversity, equity, and inclusion.