SQUAD
Technology
AppliedScientist(LLM)
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optimal for Senior candidates.
“Applied Scientist (LLM) at SQUAD. Skills: Large Language Models, Generative AI, Machine Learning, LLM Deployment. Optimize local inference. Engineer scalable RAG architectures”
What You'll Achieve.
Develop high-performance features; Optimize inference for Edge; Achieve private real-time Edge performance; Achieve scalable Cloud deployment; Culminate in technical reports; Seamlessly integrate LLM features; Ensure production readiness
Industry & Context.
Identify patterns; Troubleshoot inference
What They're Looking For.
Must Have
3+ years Machine Learning experience, NLP or LLM domain knowledge, Python3 proficiency, NumPy proficiency, Pandas proficiency, Text-processing libraries proficiency, PyTorch proficiency, Hugging Face proficiency, PEFT proficiency, Reinforcement Learning techniques proficiency, Attention mechanisms knowledge, Tokenization knowledge, Context window management knowledge, Embedding spaces knowledge, Retrieval-Augmented Generation experience, Fine-tuning experience, Agentic frameworks experience, Manage massive datasets experience, Crafting high-fidelity datasets experience, Building robust data pipelines experience, Prompt engineering expertise, Agentic framework design expertise, LLM pipeline orchestration expertise, Deploying LLMs to production experience, Written English proficiency, Spoken English proficiency
Nice to Have
Pinecone experience, Weaviate experience, Milvus experience, Chroma experience, Advanced quantization experience, Pruning experience, Knowledge distillation experience, LangChain experience, LlamaIndex experience, AutoGen experience, Web/client-server architecture understanding, Streaming API responses understanding, RAGAS experience, DeepEval experience, G-Eval experience, Docker experience, Kubernetes experience, Cloud GPU orchestration experience, C++ knowledge, Triton knowledge, CUDA knowledge
What You'll Do.
Optimize local inference
Engineer scalable RAG architectures
Engineer multi-agent systems
Generate synthetic datasets
Validate safety and alignment
Design prompt orchestration methods
Design RLHF workflows
Design autonomous agentic workflows
Identify hallucination patterns
Visualize evaluation metrics
Improve inference speed
Improve inference accuracy
Integrate LLM features
Evaluate cutting-edge techniques
Engage in technical growth
How You'll Work.
Team & Collaboration
Product teams; Data Engineering teams; Junior colleagues
Communication Scope
Technical reports; Progress reporting
Process & Methodology
Research lifecycle management
Full Job Description
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 De
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