Applied Scientist (LLM)

Team SummaryOur 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 SummaryIn 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 DutiesDesign and implement advanced methods in prompt orchestration, fine-tuning (SFT/RLHF/DPO), and autonomous agentic workflowsCurate high-quality training data from large-scale text and multi-modal sourcesIdentify patterns in model hallucinations and visualize evaluation metrics for clear interpretationTune hyperparameters and improve inference speed/accuracy through PEFT (LoRA/QLoRA) and advanced prompt engineeringCollaborate with Product and Data Engineering teams to seamlessly integrate LLM features into the broader ecosystemTrack and report progress using industry-standard benchmarks (MMLU, HumanEval, etc.) and custom internal KPIsStay at the forefront of the field (e.g., State Space Models, new Transformer variants) and evaluate cutting-edge techniques for production readinessEngage in continuous technical growth and mentor junior colleagues to elevate the team's expertise Qualifications and Skills3+ years of commercial experience in Machine Learning, with a specific focus on the NLP or LLM domainStrong knowledge of Python3, NumPy, pandas, and modern text-processing libraries, PyTorch and Hugging Face (Transformers, PEFT, Accelerate)Proficiency in PEFT/LoRA and Reinforcement Learning techniquesDeep 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 frameworksProven ability to manage and analyze massive datasets (>100GB) across text, image, and audio formatsHands-on experience crafting high-fidelity datasets and building robust data pipelinesExpertise in prompt engineering, agentic framework design, and LLM pipeline orchestrationExperience deploying LLMs to production environments using Triton Inference Server, vLLM, TGI, or ONNXGood written and spoken EnglishNice to havePractical experience with Pinecone, Weaviate, Milvus, or Chroma Advanced quantization (GGUF, AWQ, EXL2), pruning, and knowledge distillationExperience with LangChain, LlamaIndex, or AutoGenBasic understanding of web/client-server architecture and streaming API responses (Asyncio, aiohttp)Familiarity with RAGAS, DeepEval, or G-EvalExperience using Docker, Kubernetes, and cloud GPU orchestration (e.g., Run:ai, Lambda Labs)Knowledge of C++, Triton, or CUDA for custom kernel developmentWe offer multiple benefits that includeThe environment of equal opportunities, transparent and value-based corporate culture, and an individual approach to each team memberCompetitive salary packages with performance-based annual reviewsEmployment via Contract of Employment (UoP) in complete alignment with Polish Labour LawGuaranteed paid vacation, public holidays, and medical leaves as per statutory regulationsContinuous growth and development opportunities through internal knowledge hubs, corporate courses, and free English classesComprehensive private medical insurance to supplement your standard NFZ coverage.