Applied Scientist (LLM)

Stack

Responsibilities

• ▹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
• ▹Tune hyperparameters and improve inference speed/accuracy through PEFT (LoRA/QLoRA) and prompt engineering
• ▹Collaborate with Product and Data Engineering teams to integrate LLM features
• ▹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 techniques for production readiness
• ▹Mentor junior colleagues

Requirements

• ▹3+ years of commercial experience in Machine Learning, focused on NLP or LLM
• ▹Strong knowledge of Python3, NumPy, pandas, 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: 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 using Triton Inference Server, vLLM, TGI or ONNX

Nice to have

• ▹Practical experience with Pinecone, Weaviate, Milvus or Chroma
• ▹Advanced quantization (GGUF, AWQ, EXL2), pruning and knowledge distillation
• ▹Experience with LangChain, LlamaIndex or AutoGen
• ▹Basic understanding of web/client-server architecture and streaming API responses (Asyncio, aiohttp)
• ▹Familiarity with RAGAS, DeepEval or G-Eval
• ▹Experience using Docker, Kubernetes and cloud GPU orchestration (e.g. Run:ai, Lambda Labs)
• ▹Knowledge of C++, Triton or CUDA for custom kernel development

Soft skills

What we offer

• ▹Equal opportunities, transparent and value-based corporate culture and an individual approach
• ▹Competitive compensation and perks
• ▹Gig-contract
• ▹21 paid vacation days per year, paid public holidays according to Ukrainian legislation
• ▹Development opportunities: corporate courses, knowledge hubs, free English classes, educational leaves
• ▹Medical insurance from day one, sick leaves
• ▹Remote working available within Ukraine only
• ▹Free meals, fruits and snacks in the office

About the company

SQUAD's distributed team develops high-performance Generative AI features across Cloud and Edge environments.