Senior Data Scientist / ML Engineer with AWS

Robotics Technologies; California; Sur place; USD 140 000 - 230 000; Plein temps

Résumé du poste

Robotics Technologies LLC in California is seeking a Senior Data Scientist / ML Engineer with strong AWS experience to build, fine-tune, and deploy large-scale NLP and ML solutions. You will turn vast data into trainable materials, design features, and optimize distributed training on SageMaker with LoRA/QLoRA approaches.

You will own end-to-end model development from data curation and privacy safeguards to low-latency inference and production monitoring.

Qualifications

• Experience with large-scale data processing on Spark/PySpark and Hadoop ecosystem.
• Hands-on expertise with AWS SageMaker for processing, training, distributed training, and deployment.
• Proficiency in fine-tuning LLMs (LoRA/QLoRA) and reinforcement learning methods.
• Strong deep learning foundations in PyTorch, including training dynamics and multi-task setups.
• Experience with large-scale batch LLM inference and production inference.
• Knowledge of data privacy and security on AWS (IAM, KMS, Lake Formation).
• Excellent written communication with customers and science stakeholders.

Responsabilités

• Convert data to training sets, turning unstructured and structured data into narratives and synthetic data at scale.
• Design features and context to express information like market-relative price and trip context.
• Oversee large-scale distributed training on SageMaker AI (HyperPod).
• Perform fine-tuning using LoRA/QLoRA and supervised/RL training for ranking and reasoning.
• Develop deep learning models and ranking heads in PyTorch; explore co-training and multi-task setups.
• Ensure low-latency, cost-efficient inference and deployment with quantization and distillation.
• Conduct evaluation and error analysis with clear findings for customers and AWS team.
• Maintain data privacy, removing personal data where required.

Connaissances

Spark / PySpark

Hadoop ecosystem

SageMaker AI

LLM fine-tuning LoRA/QLoRA

PyTorch deep learning

LLM inference

Data privacy on AWS

Communication with stakeholders

Outils

Hive/HDFS

IAM

KMS

Lake Formation

Amazon Bedrock

vLLM

Description du poste

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.