LLMs

What You’ll Do Depending on your role, you could:

● Fine-tune, distill, and optimize LLMs for efficiency, performance, safety, and generalization.

● Build scalable, distributed architectures for large-scale AI systems.

● Design and maintain pipelines for training, validation, and deployment.

● Develop tools and processes for managing massive datasets.

● Research and apply the latest advances in LLMs, foundation models, and multimodal AI.

● Define and evaluate advanced metrics for model quality, safety, and performance.

What We’re Looking For We’re hiring across multiple seniority levels and profiles (DS, DE, ML Eng, ML Ops). Ideally, you bring experience in some of these areas:

● LLMs and Transformer development, fine-tuning, or distillation (e.g., LLaMA, Qwen, open-source and/or commercial models).

● PyTorch, HuggingFace, AWS/GCP, ETL tools, and MLOps frameworks (Kubeflow, MLflow, etc.). ● Advanced data processing (SQL, Python, Pandas, Spark, Airflow).

● Scalable architectures and distributed systems (Kubernetes, Docker, cloud computing). ●

Evaluating LLMs (safety, toxicity, bias, performance, generative quality).

● Strong technical research skills and the ability to translate cutting-edge papers into working solutions.

● Communicating complex technical findings to diverse stakeholders.

● Comfort working in fast-moving, adaptive environments, with a collaborative and results-driven mindset.

Bonus points if you have:

● Experience with RLHF, LoRA, QLoRA, PEFT, multimodal and/or foundation models.

● Contributions to the AI research community or open-source projects.