Senior Data Scientist / ML Engineer with AWS

Quantum Technologies. LLC; San Jose (CA); Presencial; USD 150.000 - 210.000; Jornada completa

Descripción de la vacante

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.

Formación

• 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).

Responsabilidades

• 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.

Conocimientos

Clear written communication

Data privacy awareness

Customer-facing communication

Herramientas

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

Descripción del empleo

Senior Data Scientist / ML Engineer with AWS

Location:

Duration:

16Months+ Extension

Domain Exposure:

Oil & Gas

Work Authorization:

• Data to training sets: turn unstructured and structured traveler and catalog data into narrative text documents and synthetic data, at a scale of tens of millions of documents.


• Feature and context engineering: decide what information goes into each document and how to express it, such as market-relative price, tier and trip context.


• Large-scale distributed training on Amazon SageMaker AI, including HyperPod.


• Fine-tuning: LoRA/QLoRA adapters, supervised training, and reinforcement learning for ranking and reasoning.


• Deep learning modeling: model architecture, ranking heads, co-training and multi-task setups.


• Inference and deployment: low-latency, cost-efficient serving, including quantization, distillation and profiling.


• Evaluation and error analysis: build evaluation sets, run ablations and catch data leakage. Write up findings clearly for the customer and the AWS science team.


• Data privacy: handle customer data safely, including removing per

Required skills:

• Spark (PySpark) and the Hadoop ecosystem (Hive, HDFS) on corpora of tens of millions of documents.


• Amazon SageMaker AI: processing, training, distributed training (HyperPod), deployment.


• LLM fine-tuning (LoRA/QLoRA) and reinforcement learning (e.g. GRPO/PPO-style methods).


• Strong deep learning fundamentals in PyTorch: architectures, training dynamics, co-training.


• Large-scale batch LLM inference and production inference (vLLM, Amazon Bedrock batch).


• Data privacy and security on AWS: personal-data removal, IAM, KMS, Lake Formation.


• Clear written communication with customer and science stakeholders.

Preferred skills:

• AWS Trainium / Inferentia (Neuron SDK).


• Recommender systems: ranking metrics (NDCG, MRR), sequential and generative recommenders.


• Embedding quantization or semantic IDs.


• Experience in a customer-facing or embedded role.

We are an equal opportunity employer. All aspects of employment including the decision to hire, promote, discipline, or discharge, will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, national origin, citizenship/ immigration status, veteran status, or any other status protected under federal, state, or local law.