Aws Machine Learning Engineer Jobs

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Today

Senior Data Scientist / ML Engineer with AWS

Robotics Technologies San Jose, CA

San Jose, CA

Today

Full time Artificial Intelligence Aws Machine Learning Big Data

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Job Description

Robotics Technologies is hiring a Senior Data Scientist / ML Engineer to turn traveler and catalog data into narrative text and synthetic datasets, then use those materials to power large-scale deep learning and LLM pipelines on AWS. The role emphasizes distributed training, careful evaluation and error analysis, and production-focused inference that balances latency and cost.

What you’ll do

Transform data into training sets: convert both unstructured and structured traveler and catalog data into narrative text documents and synthetic data at a scale of tens of millions of documents.
Drive feature and context engineering: determine what information is included in each document and how it is expressed, including items like market-relative price, tier, and trip context.
Lead large-scale distributed training: run distributed training on Amazon SageMaker AI, including HyperPod.
Perform LLM fine-tuning: implement LoRA/QLoRA adapters and run supervised training and reinforcement learning approaches for ranking and reasoning.
Build and tune deep learning models: design model architectures, develop ranking heads, and support co-training and multi-task setups.
Deploy efficient inference: deliver low-latency, cost-efficient serving, including quantization, distillation, and profiling.
Evaluate and analyze errors: create evaluation sets, run ablations, detect data leakage, and document results clearly for customers and the AWS science team.
Handle data privacy and security: manage customer data safely, including personal-data removal (details continue in the privacy focus area).

Requirements

Big data processing: Spark (PySpark) and the Hadoop ecosystem including Hive and HDFS for corpora of tens of millions of documents.
AWS ML workflows: Amazon SageMaker AI processing, training, distributed training with HyperPod, and deployment.
LLM training methods: LoRA/QLoRA fine-tuning and reinforcement learning approaches such as GRPO/PPO-style methods.
Deep learning expertise in PyTorch: experience with architectures, training dynamics, and co-training.
Production inference at scale: batch and production inference using vLLM and Amazon Bedrock batch.
AWS privacy and security: personal-data removal, and controls including IAM, KMS, and Lake Formation.
Clear communication: written communication with customer and science stakeholders.

Technologies you’ll work with

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 / Inferentia via Neuron SDK.
Recommender systems experience, including ranking metrics such as NDCG and MRR, plus sequential and generative recommenders.
Experience with embedding quantization or semantic IDs.
Background in a customer-facing or embedded role.

Location: Sanjose, CA (onsite).

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