> Markdown version of [/jobs/ext/4620100-senior-data-scientist-llm-fine-tuning-recommendation-systems-san-jose-ca-2days-week](https://www.wearedevelopers.com/jobs/ext/4620100-senior-data-scientist-llm-fine-tuning-recommendation-systems-san-jose-ca-2days-week). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Senior Data Scientist LLM Fine-Tuning & Recommendation Systems || San Jose, CA (2days / week)

- **Company:** Key2Source INC
- **Location:** San Jose, CA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Big Data, Software Code Optimization, Distributed Systems, Apache Hadoop, Apache Hive, Machine Learning, Recommender Systems, Unstructured Data, QLoRA, Normalized Discounted Cumulative Gain, Pytorch, Transfer Learning, Apache Spark, Model Validation, Discretization, AWS Trainium, Pyspark, HuggingFace, VLLM
- **Published:** November 7, 2026
- **Apply:** https://www.dice.com/job-detail/63142dd3-2dd5-4226-a744-4f0bc8945071

## About the Role

* Strong LLM Fine-Tuning experience (LoRA, QLoRA, PEFT, SFT) required
 * Hands-on experience working with model weights, transformer training loops, and model adaptation
 * PyTorch and Hugging Face Transformers
 * RLHF, DPO, PPO, or GRPO
 * Recommendation Systems, Ranking Models, Personalization
 * NDCG, MRR, MAP and model evaluation frameworks
 * Amazon SageMaker
 * PySpark, Spark, Hive, Hadoop
 * Statistical Modeling & Machine Learning

Preferred Skills

 * SageMaker HyperPod
 * vLLM, Quantization, Distillation
 * AWS Trainium / Inferentia
 * Experience building large-scale recommender systems

## Description

Seeking a hands-on Senior Data Scientist with strong expertise in LLM Fine-Tuning, Recommendation Systems, and Ranking Models. The ideal candidate must have proven experience training, adapting, and evaluating transformer-based models using large-scale datasets, with deep knowledge of recommendation science and model optimization on AWS., * Build large-scale training datasets from structured and unstructured data.

 * Fine-tune foundation models using LoRA, QLoRA, PEFT, and SFT.
 * Develop recommendation, personalization, and ranking models.
 * Design experiments and evaluate model performance using NDCG, MRR, MAP, and other ranking metrics.
 * Perform error analysis, model benchmarking, and data quality assessments.
 * Train and optimize models on Amazon SageMaker and distributed environments.
 * Collaborate with science teams to improve ranking, reasoning, and recommendation quality.