Senior Data Scientist / ML Engineer with AWS

Ethereum Technologies; San Jose (CA); Sur place; USD 150 000 - 210 000; Plein temps

Résumé du poste

Ethereum Technologies seeks a Senior Data Scientist / ML Engineer with AWS expertise to design scalable AI pipelines in San Jose. You will build data-to-document pipelines, incorporate large-scale training on AWS SageMaker, and fine-tune models using LoRA/QLoRA while ensuring data privacy.

The role emphasizes distributed training, building robust inference services, and collaboration with stakeholders to deliver customer-ready AI solutions.

Qualifications

• Experience with Spark PySpark and Hadoop ecosystem at scale (tens of millions of documents).
• Proficient in Amazon SageMaker AI for processing, training, distributed training, deployment.
• Strong PyTorch fundamentals including architectures, training dynamics, and multi-task setups.

Responsabilités

• Data to training sets: convert unstructured and structured data into narrative documents and synthetic data at scale.
• Feature engineering: decide what information goes into each document and how to express it.
• Large-scale distributed training on SageMaker AI, including HyperPod.
• Fine-tuning: LoRA/QLoRA adapters, supervised training, and RL for ranking/reasoning.
• Deep learning modeling: model architecture, ranking heads, co-training and multi-task setups.
• Inference and deployment: low-latency, cost-efficient serving with quantization and profiling.
• Evaluation and data leakage analysis; write findings clearly for customer and AWS team.
• Data privacy: ensure safe handling of customer data, including removal of personal data.

Connaissances

PySpark

Hadoop/Hive/HDFS

Amazon SageMaker

LoRA/QLoRA fine-tuning

PyTorch

LLM inference

AWS data privacy

Stakeholder communication

Outils

Hive

HDFS

KMS

Lake Formation

IAM

HyperPod

Description du poste

Senior Data Scientist / ML Engineer with AWS

Location:

Duration:

16Months+ Extension

Domain Exposure:

Oil & Gas

Client:

To Be Discussed Later

Employment Type:

W-2, 1099, C2C

Work Authorization:

US Citizen, Green Card, H-1B, OPT-EAD, CPT, Other

• 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.