Employment Type: Full-Time
Location: Zurich, on-site (hybrid)

We are seeking a highly skilled Senior Machine Learning Engineer to join our team, focusing on post-training and optimizing large language models (LLMs), their integration with retrieval-augmented generation (RAG), and multi-agent architectures. The candidate will play a crucial role in improving the core of our LLM-based solutions through applied research, innovative designs, solid implementation, systematic evaluation, and impactful optimizations. You will collaborate closely with machine learning engineers, software engineers, and domain experts to push the frontier of LLM for code generation in our products.

Key Responsibilities:

• Design LLM training datasets for instruction following, question answering, reasoning, conversational models, and code generation to improve model accuracy, robustness, and generalization.
• Lead post-training optimization efforts by integrating curated content into fine-tuning pipelines tailored to specific use cases.
• Define processes for creating and validating high-quality LLM training data (code, text, prompts, dialogues, scenarios) and synthetic data generation.
• Establish systematic evaluation and benchmarking frameworks to assess LLM performance.
• Architect, refine, and evaluate engines for retrieval-augmented generation to enhance accuracy and speed of LLM integrations.
• Develop and apply sophisticated multi-agent architectures and prompt engineering to optimize our LLM solutions.
• Lead experiments and apply the latest research directions in LLMs, ensuring impactful implementation.
• Collaborate cross-functionally with product managers, data scientists, and engineers to align solutions with business and technical goals.
• Implement high-quality LLM components, tools, and integrations, accompanied by comprehensive specifications and documentation.

Required Qualifications:

• Bachelor’s, Master’s, or PhD degree in Computer Science, Data Science, or a related field.
• 2+ years of professional experience with large language models or machine learning frameworks, specifically in post-training, fine-tuning, benchmarking, and RAG.
• Strong proficiency in Python and extensive experience with ML frameworks such as PyTorch or TensorFlow.
• Proven ability to work effectively in cross-functional teams.
• Strong analytical skills with a pragmatic approach to implementing impactful solutions.

Preferred Qualifications:

• Extensive experience with LLM architectures like BERT, Llama-3, or similar.
• Experience working with LLMs for code generation, particularly in low-resource programming languages.
• Deep understanding of reinforcement learning from human feedback (RLHF) and reinforcement learning from verifiable rewards (RLVR).
• Hands-on experience with tools for annotation, labeling, and dataset management.
• Strong understanding of bias and fairness issues in AI and practical experience mitigating these issues.
• Proficiency in TypeScript

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