Applied Scientist (LLM)

Team Summary

Our distributed team is looking for an experienced Applied Scientist with a strong background in Large Language models to develop high-performance Generative AI features across Cloud and Edge environments.

Job Summary

In this role you will drive the transition from research to production by optimizing local inference through model compression and quantization for private, real-time Edge performance, while also engineering scalable RAG architectures and multi-agent systems for Cloud deployment. Your daily responsibilities encompass the full research lifecycle, including formulating hypotheses, generating synthetic datasets, fine-tuning LLMs, and validating safety and alignment, ultimately culminating in technical reports.

Responsibilities and Duties

Design and implement advanced methods in prompt orchestration, fine-tuning (SFT/RLHF/DPO), and autonomous agentic workflows

Curate high-quality training data from large-scale text and multi-modal sources

Identify patterns in model hallucinations and visualize evaluation metrics for clear interpretation

Tune hyperparameters and improve inference speed/accuracy through PEFT (LoRA/QLoRA) and advanced prompt engineering

Collaborate with Product and Data Engineering teams to seamlessly integrate LLM features into the broader ecosystem

Track and report progress using industry-standard benchmarks (MMLU, HumanEval, etc.) and custom internal KPIs

Stay at the forefront of the field (e.g., State Space Models, new Transformer variants) and evaluate cutting-edge techniques for production readiness

Engage in continuous technical growth and mentor junior colleagues to elevate the team's expertise

Qualifications and Skills

3+ years of commercial experience in Machine Learning, with a specific focus on the NLP or LLM domain

Strong knowledge of Python3, NumPy, pandas, and modern text-processing libraries, PyTorch and Hugging Face (Transformers, PEFT, Accelerate)

Proficiency in PEFT/LoRA and Reinforcement Learning techniques

Deep understanding of attention mechanisms, tokenization, context window management, and embedding spaces

Practical experience in at least one of the following: Retrieval-Augmented Generation (RAG), Fine-tuning, or Agentic frameworks

Proven ability to manage and analyze massive datasets (>100GB) across text, image, and audio formats

Hands-on experience crafting high-fidelity datasets and building robust data pipelines

Expertise in prompt engineering, agentic framework design, and LLM pipeline orchestration

Experience deploying LLMs to production environments using Triton Inference Server, vLLM, TGI, or ONNX

Good written and spoken English