Senior Data Scientist / ML Engineer with AWS

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

Job summary

Robotics Technologies in the United States (San Jose, onsite) is seeking a Senior Data Scientist / ML Engineer to transform traveler and catalog data into narrative text and synthetic data, and to build scalable deep learning and LLM pipelines on AWS. You will work with large-scale data, develop robust models, and deploy production-ready solutions that scale to millions of documents.

You will lead distributed training with SageMaker, fine-tune with LoRA/QLoRA, optimize inference, and implement

Job description

This Senior Data Scientist / ML Engineer role at Robotics Technologies focuses on transforming traveler and catalog data into narrative text and synthetic data, then building scalable deep learning and LLM fine-tuning and inference pipelines on AWS. The position is based in Sanjose, CA (onsite) and emphasizes distributed training, rigorous evaluation, and production-focused serving.

Responsibilities

• Convert unstructured and structured traveler and catalog data into narrative text documents and synthetic data, operating at a scale of tens of millions of documents.
• Own feature and context engineering decisions, including what information each document contains and how it is expressed (for example, market-relative price and tier and trip context).
• Lead large-scale distributed training using Amazon SageMaker AI, including HyperPod.
• Drive LLM fine-tuning workflows using LoRA/QLoRA adapters, supervised training, and reinforcement learning approaches for ranking and reasoning.
• Build and iterate on deep learning models, including architecture design, ranking heads, co-training, and multi-task setups.
• Develop low-latency, cost-efficient inference and deployment strategies, including quantization, distillation, and profiling.
• Perform evaluation and error analysis by building evaluation sets, running ablations, and identifying data leakage; document findings for the customer and the AWS science team.
• Handle data privacy practices on AWS, including removing per (privacy-related personal data removal is required).

Requirements

• Experience with Spark (PySpark) and the Hadoop ecosystem, including Hive and HDFS, working on corpora containing tens of millions of documents.
• Hands-on Amazon SageMaker AI experience, including processing, training, distributed training with HyperPod, and deployment.
• LLM fine-tuning experience using LoRA/QLoRA, plus reinforcement learning methods such as GRPO/PPO-style approaches.
• Strong deep learning foundations in PyTorch, including model architectures, training dynamics, and co-training.
• Experience with large-scale batch LLM inference and production inference using vLLM and/or Amazon Bedrock batch.
• Working knowledge of data privacy and security on AWS, including personal-data removal, IAM, KMS, and Lake Formation.
• Ability to communicate clearly in writing with customer and science stakeholders.

Technologies

• Amazon SageMaker AI, HyperPod
• LoRA, QLoRA
• PyTorch
• Spark, PySpark, Hadoop ecosystem, Hive, HDFS
• vLLM, Amazon Bedrock, Amazon Bedrock batch
• IAM, KMS, Lake Formation
• Amazon Trainium, Inferentia, Neuron SDK
• GRPO, PPO
• Quantization, distillation, profiling
• Reinforcement learning

Preferred Skills

• Experience with AWS Trainium and Inferentia via the Neuron SDK.
• Knowledge of recommender systems, including ranking metrics such as NDCG and MRR, along with sequential and generative recommenders.
• Experience with embedding quantization or semantic IDs.
• Experience in a customer-facing or embedded role.