Senior Data Scientist - AWS ML Engineer (SageMaker & LLM)

Quantum Technologies. LLC; San Jose (CA); On-site; USD 150,000 - 210,000; Full time

Job summary

Quantum Technologies. LLC seeks a Senior Data Scientist / ML Engineer with AWS expertise to advance large-scale ML workflows. You will design data pipelines, engineer features, and train/distribute models on SageMaker, using LoRA/QLoRA fine-tuning and RL approaches.

Strong emphasis on data privacy and secure AWS practices. The role involves building scalable inference, evaluating model performance, and delivering clear results to customers and AWS teams.

Qualifications

• Experience with Spark/PySpark and Hadoop ecosystem handling large corpora.
• Hands-on with Amazon SageMaker AI for processing and distributed training.
• Familiar with LoRA/QLoRA fine-tuning and RL methods (PPO-style).
• Strong PyTorch proficiency and experience in LLM training pipelines.
• Experience with secure data handling on AWS (IAM/KMS).

Responsibilities

• Convert data into training sets from unstructured and structured sources.
• Perform feature/context engineering for documents and narratives.
• Set up large-scale distributed training on SageMaker (HyperPod).
• Fine-tune models with LoRA/QLoRA; supervise training and RL ranking.
• Develop deep learning architectures and multi-task setups.
• Deploy low-latency, cost-efficient inference with quantization and distillation.
• Create evaluation sets, run ablations, and document findings for clients.
• Ensure data privacy and secure handling of customer data on AWS.

Skills

Clear written communication

Data privacy awareness

Customer-facing communication

Tools

Spark / PySpark

Hadoop ecosystem (Hive, HDFS)

Amazon SageMaker AI

LLM fine-tuning (LoRA/QLoRA)

Reinforcement learning (GRPO/PPO)

PyTorch

vLLM / Bedrock batch inference

IAM / KMS / Lake Formation

Job description

Quantum Technologies. LLC seeks a Senior Data Scientist / ML Engineer with AWS expertise to advance large-scale ML workflows. You will design data pipelines, engineer features, and train/distribute models on SageMaker, using LoRA/QLoRA fine-tuning and RL approaches.

Strong emphasis on data privacy and secure AWS practices. The role involves building scalable inference, evaluating model performance, and delivering clear results to customers and AWS teams.