Data Scientist

TechDigital Group; Philadelphia (Philadelphia County); On-site; USD 150,000 - 200,000; Full time

Job summary

An innovative tech company based in the United States is seeking an experienced professional to lead the training of Large Language Models (LLMs). The ideal candidate will have a PhD or master's degree in computer science or a related field, along with over 8 years of experience in AI/ML. Responsibilities include architecting Graph-based systems and applying reinforcement learning techniques. Strong expertise in model training and high-performance computing is essential. The role also focuses on healthcare data applications.

Qualifications

• 8+ years of experience in applied AI/ML with production-grade models.
• Experience in leading end-to-end training and fine-tuning of LLMs.

Responsibilities

• Lead end-to-end training and fine-tuning of Large Language Models (LLMs).
• Architect and implement GraphRAG pipelines for contextual grounding.
• Build and scale distributed training environments using NCCL and InfiniBand.
• Apply reinforcement learning techniques to align model behavior.

Skills

LLM training and fine-tuning

Graph-based retrieval systems

Embedding models

Semantic search and vector databases

Document segmentation and preprocessing

Distributed training frameworks

High-performance networking

Model fusion and ensemble techniques

Optimization algorithms

Symbolic AI and rule-based systems

Meta-learning and Mixture of Experts architectures

Reinforcement learning

Education

PhD or master's degree in computer science, Machine Learning, or related field

Job description

Must Have

• PhD or master's degree in Machine Learning/Data Science
• Multi model agents
• Experience with text-to-image, image-to-text, speech-to-text
• Published papers in Machine Learning/Data Science journals
• Medical background project

Responsibilities

• Lead end-to-end training and fine-tuning of Large Language Models (LLMs), including both open‑source (e.g., Qwen, LLaMA, Mistral) and closed‑source (e.g., OpenAI, Gemini, Anthropic) ecosystems.
• Architect and implement GraphRAG pipelines, including knowledge graph representation and retrieval for enhanced contextual grounding.
• Build and scale distributed training environments using NCCL and InfiniBand for multi‑GPU and multi‑node training.
• Apply reinforcement learning techniques (e.g., RLHF, RLAIF) to align model behavior with human preferences and domain‑specific goals.

Qualifications

• PhD or master's degree in computer science, Machine Learning, or related field.
• 8+ years of experience in applied AI/ML, with a strong track record of delivering production‑grade models.
• Deep expertise in: LLM training and fine‑tuning (e.g., GPT, LLaMA, Mistral, Qwen); Graph‑based retrieval systems (GraphRAG, knowledge graphs); Embedding models (e.g., BGE, E5, SimCSE); Semantic search and vector databases (e.g., FAISS, Weaviate, Milvus); Document segmentation and preprocessing (OCR, layout parsing); Distributed training frameworks (NCCL, Horovod, DeepSpeed); High‑performance networking (InfiniBand, RDMA); Model fusion and ensemble techniques (stacking, boosting, gating); Optimization algorithms (Bayesian, Particle Swarm, Genetic Algorithms); Symbolic AI and rule‑based systems; Meta‑learning and Mixture of Experts architectures; Reinforcement learning (e.g., RLHF, PPO, DPO).

Bonus Skills

• Experience with healthcare data and medical coding systems (e.g., CPT, CMS, PCS).
• Familiarity with regulatory and compliance frameworks in AI deployment.