Senior Data Scientist / ML Engineer with AWS

Machine Learning Technologies, LLC; San Jose (CA); On-site; USD 150,000 - 230,000; Full time

Job summary

Machine Learning Technologies, LLC in the United States (San Jose) seeks a Senior Data Scientist / ML Engineer with AWS to build and deploy scalable AI solutions. You will work on data-to-training pipelines, large-scale SageMaker training, LoRA fine-tuning, and production inference, collaborating with customers and science teams to deliver high-impact models.

The role emphasizes data privacy, secure AWS workflows, and clear communication with stakeholders, with a 16+ month duration extension

Qualifications

• Must work with Spark (PySpark) and Hadoop ecosystem on large corpora.
• Experience with Amazon SageMaker AI: processing, distributed training, deployment.
• LLM fine-tuning (LoRA/QLoRA) and RL for ranking.
• Strong PyTorch fundamentals: architectures and training dynamics.
• Large-scale batch LLM inference and production inference.
• Data privacy and security on AWS: IAM, KMS, Lake Formation.
• Clear written communication with customers and stakeholders.

Responsibilities

• 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.
• 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

Skills

PySpark

Hadoop ecosystem

Hive

SageMaker

LoRA/QLoRA

PyTorch

LLM inference

Data privacy

Communication

Job description

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.