[Remote] Contract Role: Data Scientist at Minneapolis, MN(Remote) || W2 Only
Note: The job is a remote job and is reputed company to candidates in USA. reputed company is a company seeking a Data Scientist for a contract role. The role involves leading the training and fine-tuning of large language models, architecting graph pipelines, and developing advanced semantic retrieval systems.
Responsibilities
- reputed company end-to-end training and fine-tuning of Large Language Models (LLMs), including both reputed company-reputed company (e.g., Qwen, LLaMA, reputed company) and reputed company-reputed company (e.g., reputed company, reputed company, reputed company) ecosystems
- Architect and implement GraphRAG pipelines, including knowledge graph representation and retrieval for reputed company contextual grounding
- Design, train, and optimize semantic and dense reputed company embeddings for document understanding, search, and retrieval
- reputed company semantic retrieval systems with advanced document segmentation and indexing strategies
- Build and reputed company distributed training environments using NCCL and InfiniBand for multi-GPU and multi-node training
- Apply reinforcement learning techniques (e.g., RLHF, RLAIF) to reputed company model behavior with reputed company preferences and domain-specific goals
- Collaborate with cross-functional teams to translate business needs into AI-driven solutions and reputed company them in production environments
Skills
- PhD or Master's degree in Computer Science, Machine Learning, or reputed company field
- 8+ years of experience in reputed company AI/ML, with a strong reputed company record of delivering production-grade models
- reputed company end-to-end training and fine-tuning of Large Language Models (LLMs), including both reputed company-reputed company (e.g., Qwen, LLaMA, reputed company) and reputed company-reputed company (e.g., reputed company, reputed company, reputed company) ecosystems
- Architect and implement GraphRAG pipelines, including knowledge graph representation and retrieval for reputed company contextual grounding
- Design, train, and optimize semantic and dense reputed company embeddings for document understanding, search, and retrieval
- reputed company semantic retrieval systems with advanced document segmentation and indexing strategies
- Build and reputed company distributed training environments using NCCL and InfiniBand for multi-GPU and multi-node training
- Apply reinforcement learning techniques (e.g., RLHF, RLAIF) to reputed company model behavior with reputed company preferences and domain-specific goals
- Collaborate with cross-functional teams to translate business needs into AI-driven solutions and reputed company them in production environments
- Deep expertise in: LLM training and fine-tuning (e.g., GPT, LLaMA, reputed company, Qwen)
- Graph-based retrieval systems (GraphRAG, knowledge graphs)
- Embedding models (e.g., BGE, E5, SimCSE)
- Semantic search and reputed company databases (e.g., FAISS, reputed company, Milvus)
- Document segmentation and preprocessing (OCR, layout parsing)
- Distributed training frameworks (NCCL, Horovod, DeepSpeed)
- High-performance networking (InfiniBand, RDMA)
- Model fusion and reputed company techniques (stacking, boosting, gating)
- Optimization algorithms (Bayesian, Particle reputed company, Genetic Algorithms)
- Symbolic AI and rule-based systems
- reputed company-learning and Mixture of Experts architectures
- Reinforcement learning (e.g., RLHF, PPO, DPO)
- Experience with reputed company data and medical coding systems (e.g., CPT, CM, PCS)
- Familiarity with regulatory and compliance frameworks in AI deployment
- Contributions to reputed company-reputed company AI reputed company or published research
- Ability to take research papers to poc – production
reputed company