Job Description
• Fine-tune and optimize existing open-source LLMs (e.g., Code LLaMA, StarCoder) for code
related tasks (completion, explanation, refactoring, bug fixing).
• Process and manage large-scale, multi-language code datasets (cleaning, deduplication, quality
filtering) using distributed frameworks .
• Implement Retrieval-Augmented Generation (RAG) pipelines with vector databases for code
and technical document retrieval.
• Optimize model inference performance (quantization, distillation, GPU acceleration) for low
latency serving.
• Develop APIs/microservices to integrate AI features into IDE extensions (VS Code API,
TypeScript).
• Monitor and evaluate model performance using code-specific metrics .
• Collaborate with Data Engineers, Product, and DevOps to ensure high-quality data, scalable
deployment, and stable production performance.
• Write clean, maintainable code, manage version control (Git), and document experiments and
deployment processes.
Job Requirements
Mandatory
• Bachelor’s degree in Computer Science, AI, Machine Learning, or related field.
• 2–5 years of experience in AI/ML, with at least 1 year working on LLM or NLP
projects.
• Proficient in Python and deep learning frameworks (TensorFlow or PyTorch).
• Hands-on experience with Hugging Face Transformers and code-specific LLMs.
• Familiarity with vector databases and RAG architectures.
• Experience with Docker, GitLab CI/CD, and cloud ML services (AWS, GCP, or
Azure).
• Understanding of big data processing tools (Spark, Dask, Ray).
• Good English communication skills and ability to work in cross-functional teams.
Nice to have:
• Experience with TypeScript and VS Code extension development.
• Familiarity with AST parsing, syntax-aware embeddings, and static code analysis.
• Knowledge of MLOps tools (MLflow, W&B, Kubeflow).
• Contributions to open-source AI or coding assistant projects.
How To Apply
After application screening, the next step will be a telephone interview with a member of our HR team. If successful, the final stage is face-to-face interview that will take place in our office.
